An Exploration on Real-time Cuffless Blood Pressure …...An Exploration on Real-time Cuffless Blood...
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An Exploration on Real-time Cuffless Blood Pressure Estimation for e-Home Healthcare by Fang Wei Xuan A thesis submitted in partial fulfillment of the requirements for the degree of Master in Electrical and Computer Engineering Faculty of Science and Technology University of Macau 2011 Approved by __________________________________________________ Supervisor __________________________________________________ __________________________________________________ __________________________________________________ Date __________________________________________________________
Transcript of An Exploration on Real-time Cuffless Blood Pressure …...An Exploration on Real-time Cuffless Blood...
An Exploration on Real-time Cuffless Blood Pressure
Estimation for e-Home Healthcare
by
Fang Wei Xuan
A thesis submitted in partial fulfillment of the
requirements for the degree of
Master in Electrical and Computer Engineering
Faculty of Science and Technology
University of Macau
2011
Approved by __________________________________________________
Supervisor
__________________________________________________
__________________________________________________
__________________________________________________
Date__________________________________________________________
In presenting this thesis in partial fulfillment of the requirements for a Master's
degree at the University of Macau, I agree that the Library and the Faculty of
Science and Technology shall make its copies freely available for inspection.
However, reproduction of this thesis for any purposes or by any means shall
not be allowed without my written permission. Authorization is sought by
contacting the author at
Address: Block 3, 1/F, University of Macau, Taipa, Macau
Telephone: (853)66644506
Fax: (853)28835928
E-mail: [email protected]
Signature ______________________
Date__________________________
University of Macau
Abstract
AN EXPLORATION ON REAL-TIME CUFFLESS BLOOD
PRESSURE ESTIMATION FOR E-HOME HEALTHCARE
by Fang Wei Xuan
Thesis Supervisor: Prof. Dong Ming Chui
Department of Electrical and Computer Engineering
It was apparent to all that blood pressure (BP) is one of the most important
physiological parameters relevant for medical diagnostics, prevention as well as
therapy strategies. High BP, i.e., hypertension is the greatest risk factor for
cardiovascular diseases, including cardiac failure, coronary artery disease, and
peripheral vascular disease. The late implications are often thrombosis and embolism,
which may cause cerebral ischemia (stroke) or cardiac ischemia (heart attack). Thus,
the online monitoring and early warning message to BP are vitally important to
protect sudden heart disease and save human’s life. Conventional noninvasive BP
measurement via cuffed sphygmomanometers only provides a snapshot value, causes
circulatory interference and uncomfortable sense at the measurement position due to
wearing ballonet. However, long time monitoring can provide BP variation curve
which indicates heart status and variation tendency. Thus, continuous monitoring of
BP used in portable clinic devices is vitally important and highly cost effective in
order to detect the damage of cardiovascular system and treat them as early as
possible.
Using traditional sphygmomanometers to frequently measure BP, the encircling
band-type cuff around the arm often makes subject feel uncomfortable due to
necessary arm occlusion, thus long term BP measurement is limited because of pain
caused by blood pooling or venous congestion in the distal portion of the
measurement site. BP includes three parameters: systolic blood pressure (SBP),
diastolic blood pressure (DBP) and mean arterial pressure (MAP). As we know, by
measuring SBP and DBP one can detect hypertension and help to obtain parameters
related to cardiovascular system. MAP is also important for getting an idea about
cardiovascular system due to its close relationship to cardiac output, systemic vascular
resistance and central venous pressure. Therefore, this research focuses on developing
a real-time MAP estimation system which can be easily operated under comfortable
condition, to provide complete information for CVD diagnosis.
It is summarized from a thorough literature review that pulse transit time (PTT) based
method is competitive owing to its potential in realizing ambulatory BP monitoring
scheme for e-home healthcare. Theoretically, this method is based on the relationship
between BP and PTT and has a long development history for this relationship. It has
been explored to realize cuffless blood pressure estimation in recent years, but there
still exist problems concerning its practical applications, which can be categorized as
three parts: 1) most researchers didn’t construct a system which can automatically
adjust electrocardiogram & pulse waveform and real-time extract their feature points,
finally realize real-time PTT & MAP estimation; 2) constructing a convenient
calibration method which can be easily operated under comfortable condition is
another bottleneck problem; 3) to increase the accuracy of BP estimation is also a
bottleneck problem in real application of PTT based method.
In this thesis research, an automatic sphygmogram (SPG) fast sampling scheme with
signal conditioning circuit and relevant software for realizing signal amplitude &
baseline-shift self-adjustment and distortion control are proposed. Due to existing
external disturbance during pulse signal sampling, a close-loop control is constructed
between computer and micro control unit based on the principle of Edifier Intelligent
Distortion Control, so that to help home user quickly acquire the self-adjusted stable
pulse signal with less distortion.
To realize the real-time feature point detection, SPG and ECG waveforms are
collected to take feature point detection each few seconds. Due to existing THE
feature points mis-detection and possible loss of relative SPG or ECG waveforms
within that few seconds, a real-time PTT estimation scheme with several rules defined
to detect adjacent peak points of ECG & SPG but from different pulses is constructed,
such that to reduce PTT calculation error.
The research finding by Chinese University of Hong Kong on using contact force to
affect PTT and transmural pressure of fingertip is adopted and developed as an
external cuff pressure based calibration method, which uses three groups of external
cuff pressure on arm arterial to find out the coefficient value in BP-PTT relationship.
The prototyping system is constructed and tested, the testing result is compared with
another prevalent calibration method called hydrostatic pressure based method, which
indicates that the operation procedure of our calibration method is easier and comfort,
its accuracy for MAP estimation is comparable with that of hydrostatic pressure based
method.
Key words: Blood Pressure, Real-Time, Pulse Transit Time Based Method,
Close-loop Control, Pulse Transit Time Calculation, External Pressure Based
Calibration Method
TABLE OF CONTENTS
LIST OF FIGURES ..................................................................................................... iii
LIST OF TABLES.........................................................................................................v
GLOSSARY ................................................................................................................ vi
CHAPTER 1: Introduction ............................................................................................1
1.1 Research Background ........................................................................................1
1.2 Literature Review of Blood Pressure (BP) Measurement Methods...................3
1.2.1 Invasive BP Measurement Methods .........................................................4
1.2.2 Non-invasive BP Estimation Methods......................................................5
1.2.3 Ambulatory BP Estimation Methods ........................................................8
1.3 Literature Review of Pulse Transit Time (PTT) Based Method......................11
1.3.1 Theory Development of Relationship Between BP and PTT .................12
1.3.2 Development of PTT Based Method ......................................................14
1.4 Challenges and Goals.......................................................................................19
1.4.1 Bottleneck Problems in PTT Based Method...........................................19
1.4.2 Research Goals........................................................................................20
CHAPTER 2: Function and Architecture Design of Cuffless BP Estimation
System....................................................................................................................22
CHAPTER 3: Electrocardiogram (ECG) and Intelligent Sphygmogram (SPG)
Sampling ................................................................................................................24
3.1 ECG and Intellignet SPG Sampling Scheme...................................................27
3.2 Front-end Data Acquisition..............................................................................28
3.2.1 SPG Signal Conditioning Circuits ..........................................................28
3.2.2 Micro Control Unit (MCU) Control for Data Sampling and
Transmission ..............................................................................................35
3.3 Close-loop Amplitude and Baseline-shift Self-adjusting Method ...................37
3.4 Coding and Decoding for Realizing Two Channels Signal Recognition ........42
CHAPTER 4: Real-time PTT Calculation...................................................................44
4.1 Real-time Feature Point Detection...................................................................44
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4.2 PTT Calculation ...............................................................................................44
CHAPTER 5: External Pressure Based Calibration Method.......................................46
5.1 Moens-Korteweg Equation Deduction ............................................................46
5.2 PTT and Mean Arterial Pressure (MAP) Relationship Deduction ..................49
5.3 Theoretical Derivation of Calibration Method ................................................50
CHAPTER 6: Investigation of MAP Estimation Accuracy.........................................56
6.1 Conditions for Realizing Relationship Between MAP and PTT .....................56
6.2 Influence Factors to Precision in Proposed Calibration Method .....................59
CHAPTER 7: Testing Results and Analysis................................................................61
7.1 Calibration and MAP Measurement Procedures..............................................61
7.2 Testing of External Pressure Based Calibration Method.................................62
7.3 Testing of Adaptive Hydrostatic Calibration Method .....................................68
7.4 Comparion and Analysis Among The Testing Results....................................71
CHAPTER 8: Conclusion and Future Work................................................................72
BIBLIOGRAPHY........................................................................................................75
APPENDIX A: PUBLICATIONS...............................................................................79
APPENDIX B: PROTOTYPING SYSTEM ...............................................................81
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LIST OF FIGURES
Number Page
Figure 1. Development of BP Measurement Methods................................................4
Figure 2. Invasive BP Measurement Method .............................................................5
Figure 3. Principle and Operation of Auscultation Method........................................5
Figure 4. Automatic Auscultation Methods................................................................6
Figure 5. Read Help Auscultation...............................................................................7
Figure 6. Product (a) and Principle (b) of Oscillometric Method...............................7
Figure 7. Ambulatory Blood Pressure Measurement Methods...................................8
Figure 8. Applanation Tonometry for BP Measurement ............................................9
Figure 9. Volume Cramp for BP Measurement ..........................................................9
Figure 10. Doppler Ultrasound Method....................................................................10
Figure 11. Pulse Transit Time Based Method ..........................................................11
Figure 12. Architecture of Cuffless BP Estimation System .....................................23
Figure 13. Connection for 12-lead ECG...................................................................26
Figure 14. Pressure Sensor and Its Position on Wrist for Measuring SPG...............26
Figure 15. Placement of AgCl ECG Electrodes Separately on Back Side of
Left Leg And Right Hand ..........................................................................27
Figure 16. Structure of Home Used SPG & ECG Sampling Scheme.......................28
Figure 17. Functional Diagram of Signal Conditioning Circuit ...............................29
Figure 18. HPF, Buffer and Pre-amplifier Circuits ..................................................30
Figure 19. Summing Circuit and Inverting Amplifier Circuits.................................31
Figure 20. LPF and ADC Buffer Circuits .................................................................31
Figure 21. Bode Plot of Signal Conditioning Circuit ...............................................32
Figure 22. SNR Estimation for Original Sampled SPG (Solid Line) and
Processed SPG (Dash Line) Signals After 1st Order Butterworth
Filter ...........................................................................................................33
Figure 23. FFT of Original Sampled SPG (Solid Line) and Processed SPG
(Dash Line) Signals....................................................................................35
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Figure 24. Time Delay After Signal Processing of Original Sampled SPG
(Solid Line) and Processed SPG (Dash Line) Signals ...............................35
Figure 25. Flowchart of MCU Control Program ......................................................36
Figure 26. SPG Amplitude and Baseline-shift Self-adjusting Scheme.....................38
Figure 27. Testing Result of Prototyping System Adapted This Intelligent
SPG Sampling Scheme ..............................................................................40
Figure 28. Coding And Decoding For Realizing SPG and ECG Signal
Recognition ................................................................................................43
Figure 29. Possible Occurred 3 Cases of Feature Point Mis-detection in PTT
Calculation .................................................................................................45
Figure 30. Segment of Vessel Wall and Radius Expansion......................................46
Figure 31. The Geometry and Pressure Distribution of Brachial Artery with
Applied Cuff Pressure................................................................................51
Figure 32. Layout of The Sensing Unit Comprised An LED, A
Photo-detector, A Force Sensor. (a) Side View of The Sensing Unit
And (b) The Changes in PTT with The Increase of The Transmural
Force ..........................................................................................................54
Figure 33. The Changes in PTT with The Increase of The External Pressure..........55
Figure 34. Hardware of Prototyping System ............................................................61
Figure 35. Interface of Prototyping System on PC...................................................62
Figure 36. Graph of Monitoring MAP Results Sampled From Different
Testers on Different Days within Three Months (Symbol “S” &
“O” Indicates MAP Results Measured By Designed System and
By Oscillometric Separately) .....................................................................68
Figure 37. Hardware of Real-time Cuffless MAP Estimation System .....................81
Figure 38. Interface of Real-time Cuffless MAP Estimation System.......................82
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LIST OF TABLES
Number Page
Table 1: Comparison of Ambulatory BP Measurement Methods ............................11
Table 2: Theory Development for the BP-PTT Relationship ...................................14
Table 3: Development of Calibration Methods for BP-PTT Relationship ...............15
Table 4: Distortion Analysis Result of Butterworth Filter with Different
Orders.........................................................................................................35
Table 5: Monitoring MAP Results Sampled From Different Testers on A
Day.............................................................................................................64
Table 6: Monitoring MAP Results Sampled From Different Testers on
Different Days within A Month and Two Months Later Testing
Same Person with Previous Calibrated Parameters ...................................65
Table 7: Monitoring MAP Results Sampled From Different Testers on A
Day.............................................................................................................69
Table 8: Comparison of Testing Accuracy for MAP Estimation Using Both
Calibration Methods...................................................................................70
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GLOSSARY
CVDs. Cardiovascular Diseases
BP. Blood Pressure
SBP. Systolic Blood Pressure
MAP. Mean Arterial Pressure
DBP. Diastolic Blood Pressure
PPG. Photoplethysmography
ECG. Electrocardiogram
SPG. Sphygmogram
PTT. Pulse Transit Time
ADC. Analog-to-Digital Converter
MCU. Micro Control Unit
PC. Personal Computer
USB. Universal Serial Bus
E.I.D.C. Edifier Intelligent Distortion Control
USARTs. Universal Synchronous Asynchronous Receiver Transmitter
HPF. High Pass Filter
SNR. Signal to Noise Ratio
LPF. Low Pass Filter
THD. Total Harmonic Distortion
PCG. Phonocardiogram
PEP. Pre-ejection Period
SD. Standard Deviation
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ACKNOWLEDGMENTS
I would like to extend my sincere appreciation to my supervisor Prof. Dong Ming
Chui for his support, inspiring instruction, immense patience to my study and research.
His deep knowledge and corrections helped me in producing a more coherent and
clear thesis as well as a journal paper. I also would like to thank Mr. Lei Wai Kei, Mr.
Fei Xiao Lei and Ms. Fu Bin Bin for their valuable advices and guidance in practical
and theoretical matters throughout this research. And I thank other members of
laboratory: Dou Jia Yi, Shi Jun and Booma for their technical helps and friendships.
As far as project is concerned, I would like to acknowledge FST of University of
Macao and INESC-Macao for their financial and technical supports to the research, I
also would like to acknowledge Power System/Electronics Laboratory and
Biomedical Engineering Laboratory for hardware and facility support. My sincere
thanks also go to my classmates for their helps and encouragements, especially thanks
to Mr. Choi Wai Hei and Johnny Lao.
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DEDICATION
The author wishes to dedicate this thesis to my parents and Ms. Amy Zhang
Thanks for their support all the time!
CHAPTER 1: INTRODUCTION
1.1 RESEARCH BACKGROUND
Cardiovascular system is of great importance to human body, the corresponding
cardiovascular diseases (CVDs) are highly threatening people’s health and life. CVDs
are caused by disorders of the heart and blood vessels, including coronary heart
disease, cerebrovascular disease, hypertension, peripheral artery disease, rheumatic
heart disease, congenital heart disease and heart failure etc. For those chronic diseases
like hypertension that is quite common among the old people, it not only impairs
myocardium but also further exacerbates arteriosclerosis to stenosis. The late
implications are often thrombosis and embolism, which may cause cerebral ischemia
(stroke) or cardiac ischemia (heart attack). Thus, the online monitoring and early
warning message to cardiovascular system are vitally important to protect sudden
heart disease and save human’s life. Since blood pressure (BP) is one of the vital signs
effectively indicating the status of cardiovascular system, the need of non-invasive
and long term (up to 24-hour per day) ambulatory BP monitoring for home healthcare
is greatly raising as well.
BP is defined as stress of vessel wall when blood flows inside vessels. It provides
power to move blood inside vessels. When ventricle contracts, blood flows from
ventricle to arteries, at the moment that the highest stress of vessel wall occurring, it is
systolic blood pressure (SBP). When ventricle relaxes, arteries vessel rebound, blood
is still moving on, but BP decreases, at the moment that the stress decreased to a
minimum value, it is diastolic blood pressure (DBP). Mean Arterial Pressure (MAP)
is a term used in medicine to describe an average blood pressure in an individual
(Zheng et al., 2008). It is defined as the average arterial pressure during a single
cardiac cycle. The following formula is used for calculating MAP, which is based
upon the relationship between flow, pressure and resistance of vessel, as shown in Eq.
(1):
2
CVPSVRCOMAP +×= )( (1)
The above mentioned terms CO, SVR and CVP stand for cardiac output, systemic
vascular resistance, and central venous pressure. At normal resting heart rates, MAP
can be approximated using easier measured SBP and DBP, as shown in Eq. (2):
)(3
1DBPSBPDBPMAP −+≅ (2)
As we know, by measuring SBP and DBP can detect hypertension and help to obtain
parameters relative to cardiovascular system. MAP is also important for getting an
idea about cardiovascular system. When arterial blood goes through the body, usually
it is pumped through arteries, left in the beds of capillaries that run across the surface
of different organs and give them the nutritional substances, which are needed to
operate properly. This perfusion pressure is actually MAP. To allow an organ operate
normally, a MAP between 70 and 110 mmHg is necessary. A minimum MAP of
60mmHg is needed for proper perfusion to body organs like kidneys, brain and
coronary arteries. If the value falls below that, there is not enough blood pumping into
the organ which causes the organ to become weakness. The result will be tissue
damage to the organ, thus the measurement of MAP is also an indicator of
cardiovascular system. Even better, MAP needs to be monitored in some critical
conditions: 1) cardiac patients who are on vasodilator infusion is necessary for
monitoring MAP; 2) head-injury patients need to be monitored for MAP; 3) the
condition of septic shock also calls for MAP monitoring. In this condition, severe
infection results into decreased tissue perfusion, causing reduction in oxygen delivery
to body organs; 4) the blood pressure of a patient with dissecting abdominal aneurysm
needs to be controlled within a narrow range. Any change in the blood pressure leads
to increase in internal bleeding; it is, therefore, necessary to monitor the MAP.
Traditionally, sphygmomanometers using auscultatory and cuff-oscillometric methods
have been widely adopted to measure SBP, DBP and MAP. However, to provide
more information about health status, it requires monitoring BP frequently for long
time. Long time BP monitoring can provide blood pressure variation curve which
indicates heart status and variation tendency. Moreover, it can help to record the
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sudden change of cardiovascular system which is very helpful for physician’s further
diagnosis. Using traditionally sphygmomanometers to frequently measure BP, the
encircling band-type cuff around the arm often makes subject feel uncomfortable due
to necessary arm occlusion, thus long term BP measurement is limited because of pain
caused by blood pooling or venous congestion in the distal portion of the
measurement site. Even the so-called wrist BP monitors have been commercially
available and prevalently used for home healthcare; the practical problem of the blood
pooling mentioned above has still not been solved properly because an encircling
band-type cuff are still required. Moreover, all of these methods are based on
intermittent measurement and cannot allow a continuous measurement of pressure nor
of BP values on a beat-by-beat basis. Therefore, this research focuses on developing
an ambulatory MAP estimation system which can be easily operated under
comfortable condition, to provide complete information for CVD diagnosis.
1.2 LITERATURE REVIEW OF BP MEASUREMENT METHODS
BP measurement methods can be mainly categorized into two different groups:
invasive BP measurement and non-invasive BP measurement. In 1847, invasive BP
measurement was first used to measure a horse’s blood pressure, after that this
method generally was used during operation for monitoring in hospitals, especially in
intensive care units. However, invasive BP measurement must be in sterile condition,
also its operation is complex and risky. Therefore, scientists tried to find other
non-invasive ways to measure BP.
In 1905, Doctor Kopomkob from Soviet proposed that arteries under complete
pressed condition don’t produce any sound; otherwise they will produce sound called
Korotkoff Sound which can be listened by stethoscope. Based on this theory, a
non-invasive BP measurement method so called as auscultation was invented. After
that, several different non-invasive blood pressure measurements were developed,
such as oscillometric method, auscultatory method, applanation tonometry and
doppler ultrasound method etc. Originally, blood pressure can only be measured at
clinic. After the improvement of daily life, people care more about their health status,
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so BP measurement has been developed rapidly towards the target making BP can be
measured at home.
Nowadays, most BP measurement devices can measure SBP and DBP. However, to
provide more information about health status, it requires monitoring BP frequently for
long time. Only detecting a set of systolic and diastolic BP is not enough since it
provides limited indices for disease diagnosis. Therefore, recent research subject is to
measure ambulatory BP under comfortable condition, in order to provide complete
information for disease diagnosis. So far, the developed different blood pressure
measurement methods are shown in Fig. 1.
Figure 1. Development of BP measurement methods
1.2.1 INVASIVE BP MEASUREMNET METHOD
Invasive BP measurement measures BP by directly inserting a pipe to blood vessel
which connects to pressure pickup as shown on Fig.2. There are some advantages of
this method: 1) its output result is exactly the blood pressure; 2) it measures
ambulatory BP. However, there are some disadvantages for its operation: 1) its
measurement must under X-ray surveillance, normally used for heavy sick patients in
hospital; 2) all measurement devices must be in sterile condition.
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Figure 2. Invasive BP measurement method
1.2.2 NON-INVASIVE BP ESTIMATION METHODS
Korotkoff sound/auscultation method
Since the operation of invasive BP measurement is complex and risky, scientists
found a non-invasive BP measurement method with high accuracy which is called as
auscultation method. Now, this method is widely used in hospital by clinic
paramedics, since its accuracy can be over 90% relative to invasive measurement
method. More over, this method is used as a reference to evaluate other device’s
accuracy.
Figure 3. Principle and operation of Auscultation method
As shown on Fig. 3, the left figure is basic principle of this method. If cuff pressure is
smaller than blood pressure, blood flows inside vessel which produces corresponding
Korotkoff Sound; if cuff pressure is equal or larger than blood pressure, Korotkoff
Sound disappears. By detecting Korotkoff Sound the systolic and diastolic blood
pressures can be measured. Cuff and stethoscope are two important tools for this
method. However, this method has some disadvantages: 1) its precision mainly
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depends on paramedic’s operational experience; 2) environmental noises affect
listening to Korotkoff Sound. Therefore, based on this method, three different
automatic BP measurement methods are developed which use machine to help
listening Korotkoff Sound as shown on Fig. 4.
Figure 4. Automatic Auscultation methods
Analysis of Korotkoff sound
In 1988, Pickering invented a sensor to replace stethoscope which can record and
analyze Korotkoff Sound. According to his research, Korotkoff Sound can be divided
into three frequency components: K1, K2 and K3. K2 is a high frequency signal and
K1 & K2 are low frequency signals. At that time, K2 was detected as systolic blood
pressure, and the moment it disappears was viewed as the period of diastolic blood
pressure.
Read help auscultation
This method uses a vibration pickup to detect Korotkoff Sound, and then the
measured signal will be transferred to a buzz which produces corresponding buzz
sound. When buzz sound appears, it is systolic blood pressure; when it disappears, it
is diastolic blood pressure.
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Figure 5. Read help Auscultation
Oscillometric method
Since environmental noises affect listening of Korotkoff Sound, scientists have
invented the method so called oscillometric method to measure BP. This method uses
choc wave caused by decreasing cuff pressure, to detect diastolic and systolic blood
pressure. The amplitude of choc wave is relative to blood pressure as shown on Fig. 6
(b), if choc wave starts to increase, systolic pressure is encountered. When choc wave
achieves 0.3 of maximum amplitude, diastolic pressure is encountered. Since choc
wave is not the same as Korotkoff Sound and do not need to be listened, this method
can work in a noisy environment and does not have any requirement about cuff
position. Therefore, BP measurement instrument based on this method is often used in
hospital. However, their precision is lower than auscultation method since choc wave
is not exactly the same as Korotkoff Sound.
(a) (b)
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Figure 6. Product (a) and principle (b) of Oscillometric method
1.2.3 AMBULATORY BP ESTIMATION METHODS
After the invention of automatic BP measurement devices, BP can be measured at
home. Recent research indicates that continuous blood pressure measurement can
provide more significant information for disease diagnosis. Therefore, ambulatory
blood pressure measurement becomes one of goals for e-Home Healthcare in modern
society. Until now, there are mainly five different methods to measure ambulatory
blood pressure as shown on Fig. 7: 1) oscillometric method; 2) volume cramp; 3)
applanation tonometry; 4) doppler sound; 5) pulse transit time based method (PTT
based method).
Figure 7. Ambulatory blood pressure measurement methods
Applanation tonometry
As shown on Fig. 8, a gas chamber is used to press part of artery to be concave, at that
time inside blood pressure is equal to outside pressure. Therefore, blood pressure can
be detected by measuring outside pressure. Based on this method scientists have
already developed an artery tonometry using feedback system to make sure of artery
is flat. However, there are still three challenges to measure BP for this method: 1)
hardly to locate artery position; 2) since there are tissue and organization between
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pressure sensor and vessel wall (V.W.), the measured pressure is not exactly the blood
pressure; 3) user will feel uncomfortable if test for long time under larger pressure of
gas chamber.
Figure 8. Applanation Tonometry for BP measurement
Volume cramp
As shown on Fig. 9, a photo sensor is attached to measure photoplethysmography
(PPG) and a cuff-ballonet with adjustable pressure is tied on wrist. Changing cuff
pressure making it to be the same as blood pressure can make PPG be constant.
Through measuring PPG can detect whether the cuff pressure is equal to blood
pressure or not. Here a feedback system is used to guarantee the cuff pressure equals
to blood pressure (Boehmer, 1987).
Figure 9. Volume Cramp for BP measurement
Doppler ultrasound
As shown on Fig. 10, Doppler ultrasound method for BP measurement uses Doppler
Effect of blood flow and V.W. movement to detect diastolic and systolic blood
pressures. Signal source emits ultrasound which is reflected by blood flow, the
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received signal carries extra frequency, and through analyzing this extra frequency the
systolic and diastolic blood pressure can be detected. However, BP measurement
devices based on this method have a high cost. Therefore, there are only few products
relative to this method in market.
Figure 10. Doppler Ultrasound method
Pulse transit time based method
The right-above waveform on Fig. 11 is electrocardiogram (ECG), the lower one is
sphymogram (SPG). Normally, this method first detects peak points of ECG
waveform and the onset points of SPG waveform, and then takes their time difference
to calculate pulse wave transit time, finally calculate BP using linear relationship
between blood pressure and pulse wave transmit time (Jiao and Fang, 2002). Using
the relationship between PTT & BP to measure BP is called PTT based Method (Teng
and Zhang, 2005).
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Figure 11. Pulse transit time based method
It is summarized from a thorough literature review that till now there are mainly five
different methods of continuous BP monitoring. Table 1 lists the comparison between
each of mentioned methods. Virtually, by taking all factors into consideration
including acquisition requirement, cost, operating complexity and comfort, the PTT
based method is competitive owing to its advanced characteristics: 1) cuff-less,
potential in realizing wearable monitoring scheme for e-home healthcare; 2) easy and
suitable to wear for long time; 3) cost-effective; 4) measure beat-to-beat BP. These
characteristics indicate that the PTT based method has merits in ambulatory
monitoring BP under comfortable since its output result and operation mostly satisfy
research goals. More over, PTT based method can handle multi vital signs
simultaneously include BP, ECG, SPG, and PTT. These advantages of PTT based
method make it extremely different from others, and satisfy research goals.
Table 1. Comparison of ambulatory BP measurement methods
Methods
Items for
comparison
Oscillometric
Method
Applanation
Tonometry
Volume
Cramp
Doppler
Ultrasound
PTT Based
Method
Cuffed Yes Yes Yes Yes No
Comfort No Yes No No Yes
Cost High High Low High Low
Beat-to-Beat
BP Sampling No Yes Yes Yes Yes
1.3 LITERATURE REVIEW OF PTT BASED METHOD
PTT based method for continuous measurement of BP actually shows out the
relationship between BP and PTT hereafter termed as BP-PTT relationship. PTT is
defined as the time interval for a pressure pulse to travel from one arterial site to
another (McDonald, 1974), it explores the propagation duration of a pressure pulse
passes through a segment of the arterial tree which is usually measured as the time
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interval from the characteristic points of ECG and SPG signal in the same cycle. PTT
value was discovered to be related with BP, vessel volume and vessel wall elasticity
etc. two centuries ago, but it was Mr. Lansdowne who proposed and taken
experiments first time in 1957 to prove that: within a certain range, PTT is linear
relevant with arterial BP. After that, researchers had deduced the linear relationship
equation between BP and PTT from Moens-Korteweg equation under certain
conditions.
Although BP-PTT relationship has been discovered over 50 years, the research of its
application on BP measurement started at the end of 20th century and several
problems existed, such as calibration for the parameters in BP-PTT relationship
equation, ECG and PPG feature point detection, real-time PTT measurement etc.
Research teams proposed their methods from different aspect to improve PTT based
method be more accurate and comfortable for BP measurement, towards the
development direction of wearable monitoring scheme for e-home healthcare. Thus,
following content firstly introduces the theory development of BP-PTT relationship;
then shows the development of PTT based method on its real application, finally
presents existing problems nowadays concerning its real application.
1.3.1 THEORY DEVELOPMENT OF RELATIONSHIP BETWEEN BP AND PTT
As shown on Table 2, the quantitative analysis of blood flow started from 18th
century based on the development of hydrodynamics. In 1728, Mr. William Harvey
proposed a new word “circulatory system” which indicates that heart’s working way
with blood vessel is in a circulatory form (Milnor, 1989), after that he had done
intra-vital anatomy experiment to prove it. His achievement highly pushed the
development of blood dynamics.
In the research area of blood dynamics, blood pressure and flow phenomenal play
important roles. Mr. Poiseuille was the first one who gave detail and correct
description about the relationship between pressure and steady-state flow inside
cylindrical pipe. His contribution was called as Poiseuille’s law which described the
relationship among pressure, flow and blood vessel size. However, his assumption
13
ignored pulsation affection caused by liquid flow. For example, the acceleration of
blood with pulsation adds inertia force to the original stable state movement which
changes size of blood vessels.
In 19th century, scientists start to research the relationship between pulse wave
velocity and blood vessel wall elasticity. In 1809, by ignoring blood stickiness Mr.
Thomas Young deduced pulse wave velocity so called Young’s wave velocity. In
1878, Mr. Moens measured pressure and pulse wave velocity of rubber tube which is
full of water and proposed pulse wave propagation theory for pulse system; Mr.
Korteweg also proposed pulse wave propagation theory which considered potential
infection of fluid’s compressibility, blood vessel wall radial and longitudinal
movement (Wilmer, 1990). Coincidently, the result of Mr. Korteweg’s theory was the
same as Mr. Moens’ which ignores affection of above three factors. Therefore, Mr.
Moens’ experiment and Mr. Korteweg’s theory formed the basis of famous
Moens-Korteweg equation whose pattern was a thin wall elasticity tube. Although this
formula was based on a simple blood vessel structure, it played an important role in
blood flow dynamics which indicated the relationship between pulse wave velocity
and vessel wall’s elasticity. This equation was also the basis of relationship between
BP and PTT.
In 1898, by ignoring the blood stickiness, Mr. Lamb constructed blood vessel wall
movement equations and deduced wave velocity square’s second order characteristic
formulas (Lamb, 1932). Later, Mr. Witzig had done complete pulse wave
transmission analysis based on Mr. Lamb’s formula, his analysis first considered
about blood stickiness and blood vessel expansiveness. After that, in 1947 Mr. King
deduced the pulse wave velocity formula which considered the change of vessel
wall’s thickness.
In 1922, Mr. Bazzett discovered that the pulse transit velocity/pulse transit time is
related with blood pressure, vessel volume and vessel wall elasticity. Until 1957, Mr.
Lansdown proposed that, within a certain range, pulse transit time is linear relevant to
artery blood pressure. However, the parameters of this linear relationship vary person
14
to person since human body’s organization structure of vessel wall is different (Lu,
1995). Therefore, it is necessary to get subject’s characteristic parameters before
estimating BP.
Table 2. Theory development for the BP-PTT relationship
1.3.2 DEVELOPMENT OF PTT BASED METHOD
The basic principle of PTT based method is the relationship between BP and PTT, the
parameters of their relationship vary person to person since human body’s
organization structure of vessel wall is different. Therefore, it is necessary to get
subject’s characteristic parameters before estimating BP which is called as calibration
for BP-PTT relationship. From 1996 till now, research teams from different countries
had proposed calibration methods to make PTT based method be more accurate and
convenient for BP measurement, such as hydrostatic pressure approach (Shaltis et al.,
2004), hand elevation calibration (Shaltis et al., 2004), model-based calibration
method (Yan and Zhang, 2006) and motion based adaptive calibration (McCombie et
15
al., 2008). Their researches did actually improve the accuracy and make it more close
to the result of traditional BP measurement method. Following content elaborates
several typical calibration methods since 2000, which are chronologized in Table 3.
Table 3. Development of calibration methods for BP-PTT relationship
Based on BP-PTT relationship, Chen in Soka University deduced that: as long as the
elastic modulus of the vessel wall is maintained at a constant level, the change in SBP
can be estimated by the higher frequency component derived from the pulse arrival
time. Thus, he proposed a continuous SBP estimation method by combining two
separately obtained components: a higher frequency component obtained by
extracting & transferring a specific frequency band of PTT into BP and a lower
frequency component obtained from the intermittently acquired SBP measurements
16
with an auscultatory or oscillometric system. This method was examined in 20
patients including child, adult and elderly groups during cardiovascular surgery. A
bedside monitor (NEC BIOVIEW-4000) was used as a bio-signal acquisition
equipment, its analogue outputs were sampled at 250Hz using a personal
computer-based signal acquisition system under a LabVIEW development
environment. The estimated SBP were compared with those measured invasively
using a radial arterial catheter. The error remained within + 10% in 97.8% of the
monitoring period, the acquisition of this accuracy is under invasive intermittent BP
calibration measurement (Chen et al., 2000). Although its accuracy is high, this
method requires intermittent calibration measurement with automatic cuff inflation
and deflation at an interval of 10 or 20mins for obtaining DC component of BP is still
not comfortable and convenient for home healthcare.
In 2004, Lass et al. in Estonia proposed a calibration method which uses physical
exercise to change BP of vessel, the coefficient for the linear relationship between BP
and PTT can be estimated by measuring BP and PTT before and after exercise. To
investigate reliability of beat-to-beat BP calculation, sixty-one subjects (healthy and
hypertensive) were studied with the mean age of 42+15. PTT is calculated online, the
signals and measured PTT values are stored on a flash memory card and can later be
reviewed by PC to calculate coefficient for linear relationship between BP and PTT,
then the device can be calibrated manually to convert PTT values to BP. The average
root mean square error of estimated BP compared with Finapres method for the whole
set of subjects was 12.2+ 5.5mmHg and with auscultatory method 9.6+ 5.4mmHg
(Lass et al., 2004). In 2005, Wong and Zhang in the Chinese University of Hong
Kong (CUHK) systematically investigated the effects of exercises on the relationship
between BP and PTT. It was found that SBP and DBP increased significantly while
PTT decreased significantly immediately after exercises. Through the experiments,
PTT and BP were inversely related under the effect of two successive exercises.
Therefore, it is possible to estimate BP based on the approach after successive
exercises (Wong and Zhang, 2005).
17
Carmen in CUHK derived a simple procedure to estimate BP-PTT relationship by
modeling PTT under effects of hydrostatic pressure due to hand elevation. Eleven
volunteers were recruited to do the testing, BP & PTT were measured simultaneously
while subjects are instructed to raise their right hands such that their wrists are
0-60cm above heart level in a randomized order of steps of 15cm. Subjects were
asked to maintain each position for 15 seconds while ECG & PPG were recorded. The
results of study show that PTT changes significantly with different hydrostatic
pressure and the relationship between them generally agrees with that derived from
the theoretical model. Based on the model, it is possible to use some simple
movements such as hand elevation to calibrate the BP-PTT relationship (Carmen,
2006). However, this calibration requires subjects holding elevated hand position for
15 seconds not only causes hand ache, but also produces hand vibration which affects
accuracy of coefficient factor in BP-PTT relationship and BP measurement using
auscultatory or oscillometric method.
In 2007, McCombie in Massachusetts Institute of Technology (MIT) had developed
an enabling technology for wearable blood pressure devices that allows actuator free
self-calibration of non-invasive peripheral arterial sensors. This new technique
combined intra-arterial hydrostatic pressure variation with a novel adaptive signal
processing algorithm based on adaptive noise canceling concept (Widrow et al., 1975).
The adaptive system identification method utilized a measurable intra-arterial
hydrostatic pressure change in the sensor outfitted appendage to identify the
transduction dynamics relating the peripheral arterial blood pressure and the measured
arterial sensor signal. The proposed algorithm allows identification and cancellation
of the calibration dynamics despite unknown physiologic fluctuations in arterial
pressure during the calibration period under certain prescribed condition (McCombie
et al., 2007). Although this method can improve the accuracy of estimated BP-PTT
relationship by using modeling PTT under effects of hydrostatic pressure due to
reducing the pseudo-random pressure fluctations, it cannot solve uncomfortable
caused by hand elevation and reduce negative effect on the accuracy of BP
measurement using auscultatory or oscillometric method.
18
Haynes et al. noticed that as the cuff on the arm was inflated, the carotid-radial PWV
gradually fell and the arterial extensibility increased (Haynes et al., 1936). After that,
Driscoll and his colleagues investigated the influence of different applied brachial
recording forces on brachio-radial PWV (Driscoll et al., 1995 & 1997).
Recently, Teng et al. in CUHK has theoretically studied the effect of sensor contact
force on arterial volume and PTT. The effect of contact force between PPG sensor
and fingertip was investigated through theoretical modeling. The biomedical property
of the finger arterial wall can be described by a nonlinear arterial P-V curve
(Yamakoshi et al., 1982) which can be specified as the exponential collapse model of
the vessel proposed by Hardy et al. (Hardy and Collins, 1982). By combining P-V
curve together with the relationship equation between PTT and blood volume,
BP-PTT relationship was deduced. Simulation was performed to investigate the effect
of individual parameters on PTT in response to the applied contact force. Simulation
results indicated that PTT increases with the applied contact force, reaching the
maximum at zero transmural pressure and remaining at a constant level. To verify the
theoretical analysis, an experiment was carried out on 30 young healthy subjects (20
males and 10 females aged 20-29 years) and 6 elderly healthy subjects (2 males and 4
females aged 44-53 years). A second experiment, performed on 10 young healthy
subjects (6 males and 4 females), was carried out three months later for a repeatability
study with same experimental condition of first experiment. During experiment time,
signal was processed off-line. Both theoretical and experimental results demonstrated
that PTT increased with the contact pressure up to approximate zero transmural
pressure and maintained a near constant level in the test range of contact pressure
(Teng and Zhang, 2007).
The drawback of those calibration methods is that they still require an initial use of
conventional cuff-based devices for calibration. In this respect, Shaltis in MIT
proposed a calibration method which used hydrostatic pressure and constant sensor
band pressure instead to estimate coefficient value in BP-PTT relationship (Shaltis et
al., 2004). Following content shows its basic principle:
19
Since transmural BP (Pt) is related to the internal arterial BP (Pi), external applied
pressure (Pe) and hydrostatic pressure (Ph) due to the height difference (h) between
the measurement site and heart level, as shown in Eq. (3):
heit PPPP −−= (3)
where Pe is measurable and Ph can be approximated as product of the density of blood,
the acceleration due to gravity and h, Pi can be estimated from (1) when Pt =0. To
locate this specific instance, Shaltis used the fact that maximum pulsation occurs at Pt
=0 and recorded the PPG & ECG of the subject while they were instructed to move
their hands vertically above and below heart level. So that, the internal arterial BP and
PTT at that time can be estimated, this means that another set of data can be used for
estimation of coefficient in BP-PTT relationship.
Although above method can estimate internal arterial BP, extra tool and actuated cuff
are required to provide constant external cuff pressure and accurately identify hand’s
height above heart level. Moreover, its accuracy and procedure is worse than just
using conventional cuff-based devices. Thus, an initial use of conventional cuff-based
devices for calibration is inevitable.
1.4 CHALLENGES AND GOALS
1.4.1 BOTTLENECK PROBLEMS IN PTT BASED METHOD
Although many research teams have proposed calibration methods to improve PTT
based method’s accuracy and make it more convenient for home user, there still exist
problems concerning its real applications, which can be categorized as three parts: (1)
most researchers didn’t construct a system which can automatically adjust ECG &
PPG waveform and real-time extract their feature points, finally realize real-time PTT
estimation. They only employed medical used monitor to measure tester’s PTT value,
later on used computer to analyze recorded data and calculated corresponding BP. For
example, Chen’s testing uses a bedside monitor (NEC BIOVIW-4000) as bio-signal
acquisition equipment and Teng’s experiments processed signal off-line. Such a way
20
cannot realize real-time BP estimation and the price is too expensive for home user,
which cannot achieve the requirement of e-Home blood pressure monitoring device:
easily operate and cost-effective. Due to the existing error on feature point detection
of ECG & PPG signal and real-time PTT calculation, realizing real-time PTT
estimation with less distortion has became a difficulty issue, apparently it is one of the
bottleneck problems in real applications of PTT based method; (2) above literature
review depicts typical calibration methods including their principle and testing results.
The drawback for each calibration method is also pointed out, for example, calibration
method which gets BP-PTT relationship by changing arm’s hydrostatic pressure
requires extra high-accurate apparatus to record hydrostatic pressure’s tiny changes.
Moreover, testers need to change arm direction and hold on that positions for a
moment which is unstable and also causes uncomfortable. As a whole, constructing a
convenient calibration method which can be easily operated under comfortable
condition is another bottleneck problem in real applications of PTT based method; (3)
although Mr. Lansdown proposed that, within a certain range, pulse transit time is
linear relevant to artery BP, later on many researchers deduced more accurate and
complete BP-PTT relationship from Moens-Korteweg equation. To increase the
accuracy of BP estimation, not only the BP-PTT relationship but also many other
factors need to be considered and improved, such as the pre-ejection time which is
included in measured PTT value, hydrostatic pressure which is included in estimated
BP and so on. Thus, to increase the accuracy of BP estimation is also a bottleneck
problem in real application of PTT based method.
1.4.2 RESEARCH GOALS
Since ambulatory BP monitoring is vitally important for home healthcare, the thesis
research focuses on developing a real-time cuffless MAP estimation system for
e-Home Healthcare. After literature review of BP estimation methods, PTT based
method is competitive owing to its advanced characteristics for e-Home Healthcare.
However, above investigation indicates that there are mainly three bottleneck
problems existing in realizing BP estimation using PTT based method, by further
classification the research goals can be separated into four significant issues: (1)
21
construct a front-end data acquisition subsystem with signal amplitude and
baseline-shift self-adjusting, which helps home user quickly record stable signal; (2)
realize real-time feature point detection and PTT estimation with less distortion; (3)
design a calibration method which is easily operated under comfortable condition
based on previous research; (4) estimate factors that affects accuracy of BP estimation
either in BP-PTT relationship or in the calibration method.
CHAPTER 2: FUNCTION AND ARCHITECTURE DESIGN OF CUFFLESS BP
ESTIMATION SYSTEM
Using PTT based method, system must be able to measure real-time PTT value which
can be further used to estimate ambulatory BP. Since PTT is defined as time interval
from the peak of ECG R-wave to the onset point of pulse wave on periphery arterial,
ECG & pulse wave are two necessary input vital signs in the system, they need to be
sampled at the beginning. The piezoelectric ceramics is selected to acquire SPG signal
from wrist. To display the estimated BP value and guide home user following
designed calibration procedure to get the value of parameters in BP-PTT relationship,
a man-machine interface in personal computer is also constructed, thus designed
system is a composite of two parts: hardware and software, hardware includes ECG
and SPG sampling circuit, multi-channel acquisition circuit and USB cable; software
parts includes feature point detection, real-time PTT & BP calculation and a
man-machine interface. Compared with totally hardware designed, hardware and
software co-design high reduces total cost and makes design be simplified.
As shown on Fig.12, the structure of proposed system consists of mainly three
function modules: (1) front-end data acquisition and transmission; (2) real-time PTT
calculation; (3) MAP estimation and display. The SPG and ECG signal are firstly
sampled by biomedical sensors and sent into a signal conditioning circuit, which
processes ECG & SPG signal as ones within analog-to-digital (ADC) required range.
After that, the analog SPG & ECG are digitized in micro control unit (MCU) and
extra-codes are added into signals for data transmission to personal computer (PC).
ECG and SPG waveforms will be recognized by a decoding method on PC. A
developed SPG sampling and self-adjusting scheme is applied for SPG & ECG signal
acquisition, such that SPG signal can be automatically adjusted to satisfy sampling
criteria which contain less distortion for further feature point detection, thus home
users don’t need to spend time on finding accurate position for SPG sampling on
wrist.
23
Figure 12. Architecture of cuffless BP estimation system
Digitized SPG & ECG signal are combined together and transfer into computer
through USB cable, then ECG and SPG waveforms are recognized by a decoding
method and separately transferred into feature point detection to find out ECG
R-wave’s peak points and the onset point of SPG waveform. To approach real-time
PTT estimation, SPG and ECG waveforms are collected to take feature point
detection and calculate PTT value each few seconds. Due to existing feature points
mis-detection and possible loss of relative SPG or ECG waveforms within that few
seconds, several rules are defined to detect adjacent peak points of ECG & SPG but
from different pulses, such that to reduce PTT calculation error. After that, system
instruct home user execute designed calibration procedure to get the value of
parameters in BP-PTT relationship and calculate beat-to-beat BP. Finally, a user
friendly interface is constructed by C++ language to display measured SPG & ECG
waveform and show out estimated MAP.
CHAPTER 3: ECG AND INTELLIGENT SPG SAMPLING
Pulse signal can be sampled from different positions on human body, such as finger
tip, wrist, chest, leg etc. Since photo reflective sensor has been developed in recent
years, most researchers choose finger tip as measurement position [3-6], such as
ring-type PPG signal measurement device proposed by Chinese University of Hong
Kong. However, such a way is uncomfortable due to the space between fingers is
limited for a ring-type device, which contains signal conditioning and wireless
communication circuits and battery. Moreover, PPG has flatter morphological shape
which is not adequate for searching feature points. Selecting other positions, such as
chest and leg cannot get stronger signal, plus sticking the sensor on skin is even more
uncomfortable. Alternatively, position on wrist has strong pulse signal which can be
easily found out by most people, better still entire measurement device can be
miniaturized as a watch-type, thus has extensive application foreground in e-home
healthcare. Consequently, location of radial artery is selected for pulse acquisition.
The hospital used medical instruments having SPG acquisition function from wrist
generally is large and the price is too expensive for home user. In addition, they need
professional to adjust system parameters and record signal. To tackle those problems,
a home used SPG sampling scheme with signal amplitude & baseline-shift
self-adjusting technology and signal distortion control is proposed in this paper, which
can record a stable SPG waveform and transmit it to computer through universal
serial bus (USB). Edifier Intelligent Distortion Control (E. I. D. C.) is an intelligent
distortion control and protection technology for playing music (Edifier, 2006). Its
working principle is that the micro control unit makes distortion analysis on sampled
signal; feedbacks modification commends on adjusting amplification factor of
front-end circuit, thus to make amplitude of sampled signal within the threshold value
with low distortion. Consequently, the basic principle of this technology is developed
and applied into the designed SPG sampling scheme to help home user recording
quickly a stable and suitable SPG waveform from wrist.
25
ECG is a transthoracic interpretation of the electrical activity of the heart over a
period of time, as detected by electrodes attached to the outer surface of the skin and
recorded by a device external to the body. The recording produced by this
noninvasive procedure is termed an electrocardiogram. By definition, a 12-lead ECG
will show a short segment of the recording of each of the 12-leads, normally hospital
records 12-lead ECG for diagnosis. This is often arranged in a grid of 4 columns by
three rows, the first columns being the limb leads (I, II and III), the second column the
augmented limb leads (aVR, aVL and aVF) and the last two columns being the chest
leads (V1-V6). Ten electrodes are used for a 12-lead ECG. The electrodes usually
consist of a conducting gel, embedded in the middle of a self-adhesive pad onto which
cables clip. Sometimes the gel also forms the adhesive. They are labeled and placed
on the patient’s body as shown on Fig. 13.
In order to measure PTT, the ECG and SPG must be sampled simultaneously. Fig.14
shows pressure sensor for sampling SPG and its measurement position on wrist. To
measure ECG, stick two AgCl ECG Electrodes separately on back-side of left leg and
right hand as shown on Fig.15. The ECG waveform of limb leads II is measured.
26
Figure 13. Connection for 12-lead ECG
Figure 14. Pressure sensor and its position on wrist for measuring SPG
27
Figure 15. Placement of AgCl ECG electrodes separately on back side of left leg and
right hand
Following content firstly introduces scheme structure and depicts each module; then
explains the designed signal conditioning circuit and estimates the signal distortion;
after that shows software flowchart for signal control & transmission; finally presents
intelligent SPG sampling scheme and shows its testing result.
3.1 ECG AND INTELLIGENT SPG SAMPLING SCHEME
A filmy passive piezoelectric transducer with 3.5cm diameter and 0.5mm thick is
constructed as SPG acquisition sensor which transfers mechanical oscillation to
electrical signal through piezoelectric effect. His allowed pressure range is from -500
to 5000mmHg with sensitivity of 2000µV/mmHg. Elastic band is used to attach
transducer on wrist.
As shown in Fig. 16, the scheme consists of six functional modules. The piezoelectric
transducer transfers pulse signal to electrical waveform. Through signal conditioning
circuit the amplitude of this SPG signal is processed as the one within
analog-to-digital required range 0-5V. The signal conditioning circuit includes
pre-amplifier, baseline-shift and filtering circuits. After that, the analog SPG is
digitized in MCU module by using ATMEGA88V, which contains six 10bit
successive-approximation-type ADC input channels. MCU module is also designed as
signal processing and transmission unit since it supports simple math calculation and
has two programmable USARTs (universal synchronous asynchronous receiver
transmitter).
28
Output signal of MCU is sent to USB interface module through USART, where the
latest device FT232R is selected. Software is constructed to graph and analyze the
amplitude & distortion of digitized SPG waveform on computer, design digital filters
to further suppress distortion. In the meanwhile it sends information of waveform
amplification and baseline-shift level back to MCU to adjust digitizing SPG signal.
This close-loop feedback endows scheme with intelligent capability which can
self-adjust signal’s amplitude & baseline and minimize signal’s distortion.
The onset point of SPG signal is lower than 0V, which indicates that the operational
amplifier needs ±5V power supply, plus the ADC’s reference voltage is 5V, using
Max1680 chip (switched-capacitor voltage converter) and through USB port,
computer provides such required powers.
Figure 16. Structure of home used SPG & ECG sampling scheme
3.2 FRONT-END DATA ACQUISITION
3.2.1 SPG SIGNAL CONDITIONING CIRCUITS
The transducer sampled SPG signal often exists high-frequency interference noises
and the baseline-shift affected by tightness of elastic band. The signal conditioning
circuit processes SPG signal as the one within ADC required input range and filters
out noises. A simulation software called Multisim 8 is used to analyze circuit
performance which offers bode plot and distortion analysis.
29
As shown in Fig. 17 and 18, a 1st order high pass filter (HPF) with 0.0008Hz cut-off
frequency and large loading impedance (20MΩ) is designed to reduce signal’s DC
offset. The buffer circuit offers high input impedance and low output impedance.
Then a pre-amplifier is added to increase SPG signal amplitude and signal to noise
ratio (SNR). Subsequently, a summing circuit is designed to shift signal minimum
points to above 0V. Due to noise amplitudes are also increased after using
pre-amplifier and summing circuit, a low pass filter (LPF) with 40.8Hz cut-off
frequency is designed to reduce noises. Finally, ADC buffer is used to provide low
output impedance.
Figure 17. Functional diagram of signal conditioning circuit
The frequency of SPG signal varies from 0.03Hz to 40Hz, thus a 10Hz sinusoidal
signal with 0.6V offset and 0.9V peak-to-peak amplitude is used for simulation in
Multisim. Eq. (4) determines HPF resistive and capacitive values. In Fig. 18, buffer
circuit adds equivalent resistors to inverting and non-inverting nodes which
compensate voltage drop caused by bias current and reduce total harmonic distortion
(THD) by 0.017%.
RCf c
π2
1= (4)
where fc is cut off frequency, R and C are corresponding resistor and capacitor in HPF
circuit.
The maximum peak-to-peak output voltage of operation amplifier (TL064) is 8V and
the voltage value of SPG signal is in the range of -0.35 to 0.9V with amplitude from
0V to 1.0V. To satisfy ADC required range 0-5V and increase SNR, SPG signal is
30
amplified by gain 3.72 so that let its amplitude be close to 4V. Eq. (5) determines
resistive values in pre-amplifier circuit.
34
343938
R
RRRA o
++= (5)
where Ao is gain, R38, R39 & R34 are corresponding resistors in pre-amplifier circuit.
Figure 18. HPF, buffer and pre-amplifier circuits
After pre-amplifier, the voltage value of SPG signal is in the range of -1.53V-3.72V
with baseline located at 0V and noise amplitude is increased from 180mV to 785mV.
Thus a summing circuit and an inverting amplifier are integrated together as shown on
Fig. 19 to shift up baseline about 1.54V. Fig. 20 shows LPF and ADC buffer circuits.
Through a 1st order LPF, noises can be reduced. All determined parameter values,
that defined through simulation first and further adjusted by hardware experiment are
clearly marked in Fig. 17-20.
31
Figure 19. Summing circuit and inverting amplifier circuits
Figure 20. LPF and ADC buffer circuits
The bode plot shown in Fig. 21 indicates the effect of conditioning circuit on sampled
SPG signal. When spectrum of SPG signal varies from 0.001Hz to above 40Hz, its
amplitude decays quickly after 40Hz. The phase shift is zero degree within 0.03-1Hz
and starts to increase after 1Hz, reaches 31 degree at 40Hz. Eq. (6) transfers phase
shift at 40Hz to delay time as 2.08ms which is within the region of hardware testing
result: 1-5ms. Hardware testing also indicates that 1st order LPF reduces noise by
425mV. The refractory missions of MCU are that controls signal time delay to less
than 50ms, in the meanwhile keeps SNR larger than 20dB, thus output SPG signal is
further processed by Butterworth filter designed in PC to reduce noise and increase
SNR.
32
Fig.22 shows simulation result, the amplitude of processed wave is successfully
amplified by 3.72 with baseline located at 1.5V. Fig.23 compares original sampled
and processed SPG signals after 1st order Butterworth filter, the amplitude of
processed SPG is within ADC required range (0-5V) with baseline located at about
1.5V which is consistent with simulation result. Its amplification is also very close to
3.72. Fig.23’s left side assesses the amplitude of original and processed SPG signals
when right side assesses the amplitude of background noise inside SPG sensor and
signal conditioning circuit. The background noise is measured at the output terminal
of original and processed SPG signals by oscilloscope (Agilent 5000) when there is
no SPG signal at the input terminal. Eq. (7) calculates SNR which is increased from
13.58dB to 31.39dB after signal processing and satisfies design requirement (>20dB).
x
ΦT
1
360= (6)
where Φ is phase delay in degree, Tpd and fx are corresponding propagation delay time
and frequency.
)(log20 10
noise
signal
dBA
ASNR = (7)
where SNR is signal to noise ratio in dB, Asignal and Anoise are amplitude of signal and
noise individually.
33
Figure 21. Bode plot of signal conditioning circuit
Am
pli
tude
(V)
Figure 22. SNR estimation for original sampled SPG (solid line) and processed SPG
(dash line) signals after 1st order Butterworth filter
Curve fitting method is the process of constructing a curve or mathematical function,
which has the best fit to a series of data points, possibly subject to constraints. It can
also calculate the digital signal distortion using least square method to estimate total
difference between measured signal and pure signal. Phase shift is any change that
occurs in the phase of one quantity, or in the phase difference between two or more
quantities, normally phase shift is used as an index to evaluate waveform distortion.
Thus, curve fitting method and phase shift are used together to track tiny change
(small distortion) of SPG waveform. Processed SPG signal is firstly converted back to
original waveform by reversed mathematical calculation of simulation process, then
its FFT is calculated and compared with the one of original SPG signal as shown in
Fig.9. Normally Eq.(8) in least square method is used to find out the least distance
between two functions: f(x) and g(x). Since the spectrum of SPG signal varies from
0.001Hz to 40Hz, here Eq.(8) is applied to calculate distortion factor within this
region which indicates distortion degree of SPG waveform after signal conditioning.
∑=
−=n
i
ii xfxgR0
2
2 ))()(( (8)
where R2 is distortion factor, n is selected points on boundary area, g(x) and f(x) are
two functions under comparison.
34
Fig.24 estimates time delay by calculating distance between the maximum points of
SPG waveform. Table 4 lists time delay, distortion factor and SNR for different
Butterworth filter orders: SNR and time delay increases with larger filter order,
distortion factor is minimized at 3rd order. Comparing their values with distortion
criterion and balancing above three parameters, distortion analysis will find out the
proper filter order which minimizes distortion. Selecting other filter types results
different distortion level.
Figure 23. FFT of original sampled SPG (solid line) and processed SPG (dash line)
signals
35
Figure 24. Time delay after signal processing of original sampled SPG (solid line) and
processed SPG (dash line) signals
Table 4: Distortion analysis result of Butterworth filter with different orders
Filter Order Time Delay (s) Distortion Factor SNR_O (dB) SNR_P (dB)
1 0.03 156 13.6 31.4
2 0.04 101 13.6 36.4
3 0.06 90.6 13.6 37.7
4 0.07 92.9 13.6 38.2
SNR_O & SNR_P represent signal to noise ratio for original and processed SPG waveform
individually
3.2.2 MCU CONTROL FOR DATA SAMPLING AND TRANSMISSION
Microcontroller ATMEGA88V supports C language in-system programming, thus it
is programmed to control ADC, signal conditioning, timing and USB data
transmission. Fig. 25 shows flowchart of MCU Control Program.
36
Figure 25. Flowchart of MCU control program
In initial setting of MCU, the sampling frequency of ADC is set to 1000Hz and baud
rate to 38400 bps. Timer and the interrupt receiver are all enabled. Then MCU control
program waits for interrupt signal. Once timer counter equals 1ms, timer is interrupted
and set CPU to sleep mode. MCU carries ADC once with less power consumption
and obtains smaller noise from I/O periphery equipment due to CPU is in sleep mode.
Subsequently, ADC interrupt wakes up MCU and stores digitized SPG data. Finally,
MCU carries signal conditioning and filter design according to received control code
and send out finalized digitized SPG data using USART. During whole procedure,
once get the receiving interrupt MCU stores control code sent from PC.
37
3.3 CLOSE-LOOP AMPLITUDE AND BASELINE-SHIFT SELF-ADJUSTING
METHOD
Ideally SPG baseline can be stably located at 0V after signal conditioning, but the
home-user’s improper operation in SPG measurement or selecting bad position might
shift SPG waveform to saturation or cutoff area and record wrong SPG signal. To
tackle such an uncertainty and imprecision problem, an intelligent SPG sampling
scheme is proposed in this paper, which automatically adjusts SPG baseline and
amplification level, minimizes SPG distortion, lets SPG waveform totally satisfy
sampling criteria and records SPG signal.
C++ program is constructed to realize this intelligent sampling function and automatic
SPG recording. Once receives SPG waveform from MCU, it compares its amplitude
with sampling criteria and analyzes signal distortion by calculating & comparing SNR,
time delay and distortion factor for different filter order every second, then selects a
filter order which balances above three distortion parameters. The highest point of
SPG waveform is required to be larger than 4V but smaller than 5V; the lowest point
must be lower than 1V but larger than 0V. When these sampling criteria are satisfied
stably and continuously for 10 seconds, software system will start recording SPG
waveform occurred in these 10 seconds. If the sampling criteria cannot be satisfied,
software system starts to analyze amplitude and baseline of SPG waveform, and
feedbacks control code to MCU. This hardware and software integrated, analysis and
feedback loop between computer and MCU form a close-loop control which speeds
up the sampling and guarantees the quality of sampled SPG waveform. Obviously it is
a prominent brightness in such a novel scheme.
38
Figure 26. SPG amplitude and baseline-shift self-adjusting scheme
Notation:
A1: the SPG highest point is located in 4V-5V, use amplification gain 1;
A2: the SPG highest point is located in 3.5V-4V, use amplification gain 1.2;
A3: the SPG highest point is located in 3V-3.5V, use amplification gain 1.4;
B1: the SPG lowest point is located in 1.5V-2V, shift baseline down 1.5V;
B2: the SPG lowest point is located in 1V-1.5V, shift baseline down 1V;
B3: the SPG lowest point is located in 0V-1V, do not shift baseline.
Actually, the highest and lowest points of SPG waveform are separately used to
estimate amplification and baseline adjusting degree. Three ranks of amplification
degree A1, A2, A3 and three ranks of baseline-shift degree B1, B2, B3 are defined.
As shown in Fig. 26, software system analyzes the amplitude of input SPG waveform,
classifies its highest and lowest points according to above defined ranks. This analysis
follows two rules: 1) if the highest point is larger than 5V, the amplification degree
decreases one rank. 2) if the lowest point is lower or equals to 0V, the baseline-shift
degree increases one rank. Therefore, the software system determines amplification
and baseline-shift adjusting rank and feedbacks the control code to hardware MCU to
adjust its digitizing SPG signal accordingly. This self-adjusting happens every second
until the SPG waveform satisfies the sampling criteria. If the highest and lowest
39
points of SPG waveform are not located at defined ranges, which is caused by the
elastic band is too tight or too loose, or the measurement position is wrong, then the
software system will show out a message to notice user to re-tie elastic band or adjust
the measurement position on wrist. Fig. 27 shows the results of prototyping system
adapted this intelligent SPG sampling scheme, which are obtained from testing five
input cases of improper operation. On waveform a), SPG suddenly shifts down its
base line at 3.7s, the intelligent sampling scheme makes SPG waveform stable after 2s
(from point 4s to 6s) adjustment; On waveform b), SPG waveform suddenly shifts up
to saturation after 3s, the intelligent sampling scheme makes SPG waveform stable
after about 1s (from point 3.5s to 4.5s) adjustment; On waveform c), intelligent
sampling scheme finds out sampled SPG waveform is too weak, then automatically
amplifies SPG waveform step by step till it satisfies sampling criteria after about 5s
(from point 1s to 6s) adjustment; On waveform d), the amplitude of sampled SPG
waveform is too big and over cutoff area, intelligent sampling scheme finds it out and
reduces its amplitude step by step which take 5s (from point 0s to 5s) to reach
sampling criteria; On waveform e), the tester deliberately and arbitrarily shakes his
hand from 3s to 7.5s, and then puts hand stable. The intelligent sampling scheme only
takes 2.5s (from point 7.5s to 9s) to adjust SPG and makes it stable. The adjusting
time (1s to 6s) for above five cases does not slow down the SPG sampling, instead it
helps home user to record quickly a stable and accurate SPG waveform.
40
Figure 27. Testing result of prototyping system adapted this intelligent SPG sampling
scheme
Towards realizing fast SPG sampling for e-home healthcare, an intelligent sampling
scheme is designed and implemented. Each time system checks simultaneously the
amplitude and baseline-shift level of SPG signal, analyses its distortion and selects a
suitable filter order. When it is necessary, system automatically changes baseline shift
and amplitude 1, 2 or 3-level. At most such an adjustment can change amplification
and baseline shift degree six times, which is demanded by sampling method. Of
course, adding more adjustment levels can make SPG signal be adjusted more
accurately but requires longer time. Refer to comments from home users, the
adjustment time less than 10 seconds is acceptable and the implemented scheme
requires average 1 to 6 seconds only.
41
When amplitude of sampled SPG is too weak, the system cannot make proper signal
adjustment through changing its amplification but can only notice home users to retie
the elastic band on wrist. Because increasing the system amplification level would
increase the existing noise concurrently, thus requires higher order filter and longer
time delay to keep SNR in predefined range. Fortunately, most home users can easily
find out proper pulse signal position on their wrist and let the amplitude of sampled
SPG reach the value larger than 0.5V. Due to these reasons, the system amplification
adjustment is designed as 3 levels: 1, 1.2 and 1.4 times of original amplifying factor.
The time difference between the peak points of original and processed SPG waveform
should be estimated and compared such that to select automatically the different
Butterworth filter orders. However the existing noise inside of original SPG signal
may affect the accuracy of detecting peak points. Fig. 22 shows that the amplitude of
noise in original SPG signal varies from 0V to 0.1V and the amplitude of original
SPG signal varies from 0.5V to 1.0V, thus the percentage of noise inside of original
SPG signal is about 0~20% of signal, which may result in a highly inaccurate
detection of peak points. To tackle such a problem, a method of adjusting filter order
such that to reduce the time difference between the peak points of original and
processed SPG waveform to the minimum is applied. The adjustment strategy is
designed and implemented as following: when the coordinate value in time axis of
mis-detected peak point of original SPG signal is behind the peak point of processed
signal, then use filter order which has minimum time delay; when the coordinate
value in time axis of mis-detected peak point of original SPG signal is ahead of the
peak point of processed signal, then use filter order which has the maximum time
delay.
In conclusion, an intelligent SPG sampling scheme is elaborated and constructed, in
which a piezoelectric transducer with signal conditioning circuit and close-loop
control is used, thus the signal amplitude and baseline-shift can be automatically
adjusted within few seconds, and the distortion of signal is eliminated. The test results
show that this intelligent SPG sampling scheme makes significant improvement in
fast sampling SPG signal with less distortion, solve the problem existing on hands
42
free SPG fast and stable sampling, which is a new challenge to all researchers
working on e-home healthcare.
3.4 CODING AND DECODING FOR REALIZING TWO CHANNELS SIGNAL
RECOGNITION
Recently, experiments carried on health subjects show that 1ms variety of PTT
reflects 1-3mmHg BP variation which changes from person to person. Thus, the
frequency for sampling SPG and ECG signals is better to be set as close to 1000Hz
for accurately detecting BP variation. The coding method for realizing two channels
signal recognition at least requires 2 bytes codes together with each signal data during
data transmission, thus a set of sampled signal data contains 6 bytes, which means that
the data transmission rate needs to be set as 6000Hz for 1000Hz sampling frequency
of SPG & ECG signals. However, the maximum baud rate of ATMEGA88V is
38400bps which equals to 4800Hz for data transmission, consequently the system’s
maximum sampling frequency is 800Hz only and the resolution of PTT is 1.25ms.
As shown on Fig. 28, each set of data includes six bytes: 4 codes, one SPG data and
one ECG data. The adjacent ECG and SPG data are separated by two codes, before
SPG data code 1 & 2 are used which are expressed as hexdecimal digits 01 & 02 in
designed system, after that code 3 & 4 are used which are expressed as 21 & 22.
When data is transmitted to PC, PC analyzes input data each three bytes. There are six
possible combinations for each input three bytes: 01 02 SPG; 02 SPG 21; SPG 21 22;
21 22 ECG; 22 ECG 01; ECG 01 02. Among them three combinations include SPG
data and the remains include ECG data. By comparing each three input data with
these six combinations, ECG and SPG data can be recognized.
43
Figure 28. Coding and decoding for realizing SPG and ECG signal recognition
CHAPTER 4: REAL-TIME PTT CALCULATION
4.1 REAL-TIME FEATURE POINT DETECTION
The automatic delineator proposed in (Li et al., 2011) is directly applied in designed
system to find out peak points of SPG waveform. Parameter extractor of the IHHCS
proposed in (Chan et al., 2011) is applied to search peak points of R-wave in ECG
waveform. Above two methods can find out feature points with accuracy above 90%
in a static situation. To approach real-time feature point detection, system is designed
to store data and delineate feature points each 6 seconds. Thus, in 800Hz sampling
frequency, when each coming signal data reaches 4800 bytes, system automatically
uses above two methods to delineate feature points within these data and then
calculate PTT.
4.2 PTT CALCULATION
Although above real-time feature point detection has accuracy above 90%, one feature
point mis-detection for either ECG or SPG can affect other PTT calculation since it is
the time interval between adjacent peak points of ECG and SPG in the same cardiac
cycle. As shown on Fig. 29, three cases may happen when feature point mis-detection
appears: (1) in case 1, suppose ECG peak point detection misses one point, thus
calculated PTT value becomes the time interval between peak point of SPG and that
of following cardiac cycle’s ECG; (2) in case 2, suppose SPG peak point detection
misses one point, thus calculated PTT value becomes the time interval between peak
point of ECG and that of following cardiac cycle’s SPG; (3) in case 3, suppose SPG
peak point detection finds out wrong point, thus calculated PTT value becomes
abnormal and the time interval is larger or lower than normal case. To calculate true
PTT, peak points for ECG & SPG are sequenced by occurrence time. Points with
same sequence are paired to calculate time interval. Normally the health subjects’
PTT value is within a certain range from 90 to 170ms, thus it can be used to
discriminate abnormal cases. Once abnormal case is detected, following rules are
45
carried out to make correct sequence for peak points which avoids negative effect of
mis-detected points: (1) when case 1 happens, the sequence of following SPG peak
points decreases 1 but the sequence of ECG peak points remains unchanged, this
means that system abandons SPG peak point which is paired with missed ECG peak
point, thus guarantee the sequence of following peak points is correct; (2) when case 2
happens, the sequence of following ECG peak points decreases 1 but the sequence of
SPG peak points remains unchanged, this means that system abandon ECG peak point
which is paired with missed ECG peak point, thus guarantee the sequence of
following peak points is correct; (3) when case 3 happens, no matter PTT becomes
larger or smaller, both sequences of SPG and ECG increase 1, this means that system
abandon both peak points and go for calculating next PTT value. Sometimes, peak
point detection may miss two or more points, fortunately above three rules can also
deal with such complex cases, which actually are formed by above three basic cases.
Figure 29. Possible occurred 3 cases of feature point mis-detection in PTT calculation
CHAPTER 5: EXTERNAL PRESSURE BASED CALIBRATION METHOD
5.1 MOENS-KORTEWEG EQUATION DEDUCTION
Firstly, assume blood is non-viscous flowing liquid which flows inside complete
elastic cylindrical tube, and then blood vessel is as infinite segment with same axial
velocity coded as v.
To analyze hemodynamic, liquid’s segment is coded as dx, pressure wave takes dt to
pass dx, the pressure varying quantity is coded as dp, and the corresponding radius
displacement is dRi, the thickness of arterial wall is codes as h, as shown on Fig.30.
Figure 30. Segment of vessel wall and radius expansion
Then the equation for pulse wave velocity is:
dt
dxv = (9)
The power of blood flowing comes from force difference between upper and down
stream, from my opinion, using Newton Second Law:
47
dx
Adp
dx
PdA
dx
APd
dx
dF+==−
)( (10)
where P is blood pressure, A is vessel’s cross section area. Since A is not relative with
x, Eq. (10) can be expressed as:
dx
Adp
dx
dF=− (11)
The equation for cross section area is:
2
iRA π= (12)
Blood quality is dxρπRi
2 within region dx, and axial acceleration is dv/dt. Equation
(13) can be deduced based on Newton second law:
dt
dvdxRdPRamF ii ⋅⋅=−==
22)( ρππ (13)
Combining above equations, deduce relationship between blood pressure and flowing
velocity:
dt
dv
dx
dPρ=− (14)
Secondly, uses continuity equations to describe the relationship between blood flow
velocity and Young’s modulus. Although outflow velocity is smaller than inflow
velocity, based on conservation of mass, volume change dV/dt is equal to difference
between inflow volume and outflow volume coded as dQ. Then
dt
dRR
dx
dtdxdRR
dx
dtdV
dx
dQ iiii ππ 2/2===− (15)
Volume rate can also be expressed as multiplication of cross section area and instant
rate.
48
dx
dvR
dx
vAd
dx
dQ i
2)( π
−=−=− (16)
Solve these two equations, deduced the relationship between change of V.W.
thickness and flowing velocity:
dx
dvR
dt
dR ii
2=− (17)
Young’s modulus (E) is a measurement of the stiffness of an isotropic elastic material.
It is defined as the ratio of the uniaxial stress F/S (ratio between force and the area it
affects) over the uniaxial strain ∆L/L (ratio between varied size and original size) in
the range of stress in which Hooke’s Law holds.
LSFLLLSFstrain
tressE ∆=∆=
⋅= /)//()/(
1 (18)
Deduce the expression for stress:
)2(
)(
)(
)( 2
22
2
hRh
dPhR
hRR
dPhR
S
Fstress
i
i
ii
i
−
−=
−−
−==
π
π
ππ
π (19)
Since radius of V.W. is much larger than thickness of vessel wall, that is Ri >> h, Eq.
(19) can be further transformed as:
h
dPRstress i
2= (20)
Deduce expression for strain:
i
i
R
dR
L
Lstrain =
∆= (21)
Consequently, using Young’s modulus (E) definition and combining Eq. (20) and Eq.
(21), deduce expression of dRi as:
49
E
R
h
dPdR i
i
2
2⋅= (22)
Combining Eq. (14) and Eq. (22), deduce the relationship between blood
pressure and flowing velocity:
dx
dv
R
Eh
dt
dP
i
⋅=− (23)
Take derivative of x for equation (14), and also take derivative of t for equation (23):
⋅⋅=−
⋅=−
dtdx
vd
R
Eh
dt
Pd
dtdx
vd
dx
Pd
i
2
2
2
2
2
2
ρ
(24)
Solve them and get the relationship between blood flow velocity and Young’s
modulus finally:
iR
Ehv
ρ= (25)
which is the Moens-Korteweg equation based on two conditions: 1) Thickness of
V.W. is constant; 2) Vessel radius is much larger than thickness of V.W., which are
satisfied of course in most cases.
5.2 PTT AND MAP RELATIONSHIP DEDUCTION
Using exponential relationship between modulus of elasticity and BP to deduce
relationship between PTT and BP. The exponential relationship between modulus of
elasticity and BP is:
PeEE γ0= (26)
where E0 is the modulus of elasticity when pressure is zero, P is blood pressure, r
represents a characteristic of vessel which ranges from 0.016 to 0.018 (mmHg-1).
50
Inverse proportional relationship between PTT and pulse transmit velocity is:
PTT
Zv
∆= (27)
where ∆Z is pulse transmitting distance.
Then get continuity equation:
=
=
∆=
P
i
eEE
R
Ehv
PTT
Zv
γ
ρ
0
(28)
Solve them, deduce an equation between BP and PTT:
−
∆= PTT
hE
ZRP i ln2)ln(
1
0
2ρ
γ (29)
)2
exp(
)/( 2
1
0
P
RhE
ZPTT
i
γ
ρ
−∆= (30)
By replacing –r/2 with k and replacing E0h/ρRi with pwv0, Eq. (30) can be expressed
as below:
)exp(0
Pkpwv
ZPTT ⋅−
∆= (31)
5.3 THEORETICAL DERIVATION OF CALIBRATION METHOD
The transit time of the pressure pulse across an arterial segment of length Z is deduced
by combining the Moens-Korteweg equation with Hughes’ non-linear expression for
elastic modulus of the artery wall (Hughes et al., 1979), as shown in Eq. (26).
51
))(exp()(0
tPkpwv
ZtPTT tm⋅−
∆= (30)
where pwv0 and k are subject’s characteristic parameters, Ptm is the transmural
pressure acting across the artery wall (McCombie et al., 2008).
By applying an external cuff pressure Pext1 on arterial segment of length ∆ZA, its
transmural pressure is changed and pulse transmit time within this region is affected
as shown on Fig. 31. An expression of PTT after applying a constant cuff pressure
Pext1 on arm arterial is given in Eq. (27).
dztzPkpwv
ZtPTT
Z
tm )),(exp(1
),(0 0
∫∆
⋅−=∆ (31)
Cuff
0
Pext
Z
Pext1
Figure 31. The geometry and pressure distribution of brachial artery with applied cuff
pressure
Without external cuff pressure on arm arterial, the transmural pressure equals to mean
blood pressure PMAP, which is a term used in medicine to describe an average blood
pressure in an individual. It is defined as the average arterial pressure during a single
cardiac cycle (Zheng et al., 2008). When external cuff pressure is larger than MAP,
blood vessel will be squashed and its radius is diminished; when external cuff
pressure is smaller than MAP, only transmural pressure is reduced. Thus an external
52
pressure which is smaller than MAP is applied on arterial segment and slows down
pulse transmit velocity, such that to make the pulse transit time within this arterial
segment be increased. Eq. (28) shows calculation of pulse transit time PTText1 after
adding external cuff pressure Pext1.
)exp(
))(exp(
0
1
0
1
MAPA
extMAPA
ext
kPpwv
ZZ
PPkpwv
ZPTT
−∆−∆
+
−−∆
=
(32)
By replacing ∆Z/pwv0 with y0, there are two unknown parameters: y0 and k for
describing relationship between BP and PTT. After adding external cuff pressure, one
more unknown parameter ∆ZA/pwv0 is added to the equation and it is tough to
determine two unknown parameters by solving Eq. (26) & (28), even pulse transmit
time with and without external cuff pressure are measured. By changing external cuff
pressure, Eq. (33) is obtained with known value PTText2 and Pext2, by substituting Eq.
(26), (28) & (29) into the left side of Eq. (30), the equation for calculating parameter k
can be obtained which eliminates unknown parameters ∆Z/pwv0 and ∆ZA/pwv0
existing in Eq. (31).
53
)exp(
))(exp(
0
2
0
2
MAPA
extMAPA
ext
kPpwv
ZZ
PPkpwv
ZPTT
−∆−∆
+
−−∆
=
(33)
1/
1/
1)exp(
1)exp(
1
2
1
2
1
2
−
−=
−⋅
−⋅=
−
−
PTTPTT
PTTPTT
Pk
Pk
PTTPTT
PTTPTT
ext
ext
ext
ext
ext
ext
(34)
)exp(/ 11 extext PkPTTPTT ⋅= (35)
By adding one known external cuff pressure to arm arterial and measuring its
corresponding PTText2, then substitute PTT value together with PTText2 and Pext2 values
into Eq. (31), unknown parameter k can be determined. In order to increase the
accuracy of parameter k, calibration method is designed to calculate average value of
parameter k under several external cuff pressures. In a practical application, one needs
only to measure MAP one time and substitutes its value together with values of k and
PTT to Eq. (26), ∆Z/pwv0 can then be determined. After knowing above two
parameters k and ∆Z/pwv0, calibration curve can be obtained through Eq. (36).
kpwv
ZPTTPMAP
1))ln((ln
0
⋅∆
−−= (36)
In 2007, Teng et al. in CUHK has theoretically studied the effect of sensor contact
force on arterial volume and PTT. The left graph on Fig. 32 shows the layout of the
sensor unit. The effect of contact force between PPG sensor and fingertip was
investigated through theoretical modeling. It should be pointed out that, for the
selected P-V model, the external contact pressure has effect on PTT only when it
increases from zero to the pressure that equals to the mean intra-arterial pressure
under all simulation conditions. When the external contact pressure is larger than the
mean pressure, it has no further effect on PTT. The right graph on Fig. 32 shows the
54
experiment their result of the changes in PTT with the increase of the transmural force
which is consistent with theoretical analysis. The P-V model for the property of the
arm arterial wall can also be described by a nonlinear arterial P-V curve (Yamakoshi
et al., 1982) which can specified as the exponential collapse model of the vessel
proposed by Hardy et al. (Hardy and Collins, 1982), thus the effect of the external
cuff pressure on PTT should be similar as the effect of sensor contact force on
fingertip. Similar result was also found in our test when the carotid-radial PWV with
different experimental environment since they estimate the effect of sensor contact
force on finger and our testing estimates the effect of external cuff pressure on arm
arterial. Six volunteers include male and female are recruited to do the testing, the
external cuff pressure is increased step by step at 20mmHg, the maximum external
cuff pressure is 140mmHg. As shown in Fig. 33, four subjects’ MAP is about
80mmHg whereas other two subjects’ MAP is about 100mmHg, the external contact
pressure has effect on PTT only when it increases from 0 to 80 and 100mmHg. When
the external contact pressure is larger than their MAP value, it has no effect on PTT.
Since only the external cuff pressure less than MAP has effect on PTT, the added
external cuff pressures in the calibration is set to be smaller than subject’s MAP. Thus,
the external pressures are set to be 0.5MAP, 0.7MAP and 0.9MAP. The reason why
external cuff pressure is selected instead of sensor contact force on the fingertip in this
research is that the orientation & contact force of the sensor is hard to be set as
constant due to its high sensitivity and using Oscillometric to measure the one time
MAP on the fingertip requires specified facility.
Pu
lse
Tra
nsi
t T
ime
(ms)
55
Figure 32. Layout of the sensing unit comprised an LED, a photo-detector, a force
sensor. (a) side view of the sensing unit and (b) the changes in PTT with the increase
of the transmural force
120
140
160
180
200
220
240
0 20 40 60 80 100 120 140Quantity of external pressure (mmHg)Quantity of external pressure (mmHg)Quantity of external pressure (mmHg)Quantity of external pressure (mmHg)PTT (ms)PTT (ms)PTT (ms)PTT (ms)
Subject 1 with 76mmHg MAPSubject 2 with 80mmHg MAPSubject 3 with 81mmHg MAPSubject 4 with 85mmHg MAPSubject 5 with 97mmHg MAPSubject 6 with 103mmHg MAP
Figure 33. The changes in PTT with the increase of the external pressure
CHAPTER 6: INVESTIGATION OF MAP ESTIMATION ACCURACY
The principle of proposed external pressure based calibration method has been detail
explained in previous chapter, after calibration the BP-PTT relationship equation can
be estimated individually, by measuring the real-time PTT value the beat-to-beat
MAP value can be calculated using this equation. Although the BP-PTT relationship
equation in Chapter 5 can be deduced from Moens-Korteweg equation, their
establishment is based on several conditions, such as blood is a kind of
incompressible fluid, the thickness of vessel wall must be a constant. PTT is defined
as the time interval for a pressure pulse to travel from one arterial site to another
(McDonald, 1974), is one of the proposed parameters for the non-invasive
beat-to-beat BP estimation. It is usually measured as the time interval from the
characteristic points of ECG and PPG signal in the same heart cycle. However, recent
research indicates that using above method measured PTT value includes PEP which
is the duration of the iso-volumetric ventricle contraction up to the aortic valve
opening (Muehlsteff, 2006), only the PTT values relate to the arterial wave
propagation has the relationship with BP. Thus, the accuracy of PTT estimation is
affected which further reduce the MAP estimation accuracy. During calibration
procedure, several influence factors cause inaccuracy on BP-PTT relationship, such as
the measurement of relative BP and PTT is not simultaneously, the external cuff
pressure is not exactly the same as designed. Above description shows out there are
some factors affecting the accuracy of BP estimation by using PTT based method,
thus following content analyzes those existing factors which affect the accuracy of BP
estimation.
6.1 CONDITIONS FOR REALIZING RELATIONSHIP BETWEEN MAP AND
PTT
Chapter 5 deduces the Moens-Korteweg equation under the following four conditions:
1) blood is a kind of incompressible fluid; 2) ignoring the effect of blood viscosity; 3)
57
the thickness of vessel wall is constant; 4) the thickness of vessel radius is much
larger than that of vessel wall. The proposed calibration method for BP-PTT
relationship is deduced from Moens-Korteweg equation, thus its accuracy is relative
to above four conditions which are estimated in the following content.
Blood is a kind of fluid which is not absolutely incompressible, but its compressibility
is extremely small by comparing with gas. Blood is hard to be compressed inside
human body. SBP in the ventricle about 120mmHg is still not enough to compress
blood. Thus, it is reasonable to assume that blood is a kind of incompressible fluid in
BP-PTT relationship equation deduction.
When blood flows inside vessel, the movement of adjacent particles cause friction
force existing between vessel wall and blood, which makes blood become a kind of
viscosity fluid. The resistance of blood flow is directly proportional to blood viscosity,
thus blood viscosity indicates a quantitative resistance existing in blood flow which
reduces the pulse transit velocity and increases PTT. Thus, the measured PTT value
contains certain quantity caused by resistance of blood flow which increases the
irrelevance in the BP-PTT relationship. Previous research has indicated that blood
viscosity has the effect on pulse transit velocity which can be ignored.
The thickness of vessel wall can remain constant for long time (several months to
several years), unless some critical diseases happening affect the blood flow inside
vessel wall and change the thickness of vessel wall. The thickness of vessel wall
becomes larger with age increased, this is because nutritive material such as
cholesterin cannot be excreted and accumulated in the vessel, after long time’s
development the thickness of vessel wall becomes larger and the elasticity of vessel
wall reduces, but this procedure takes long time which may last several years. To
reduce this effect, the proposed calibration method is designed to re-calibrate each
one month.
Most arteries contain vessel wall whose thickness is much smaller than vessel radius
(<10% of vessel radius), but some arteries’ thickness with strong muscle is larger than
58
10% of vessel radius. If the Moens-Korteweg equation’s deduction considers the
effect of vessel radius, the equation for Young’s modulus can be deduced in Eq. (37)
i
i
i
i
dR
R
hRh
dPhRE
)2(
)( 2
−
−= (37)
Then Eq. (24) can be changed into Eq. (38).
⋅⋅
−
−=−
⋅=−
dtdx
vd
hR
hREh
dt
Pd
dtdx
vd
dx
Pd
i
i
2
22
2
2
2
2
)(2
)2(
ρ
(38)
Solve them and get the new Moens-Korteweg equation finally:
)(2
)2(
hR
hREhv
dt
dx
i
i
−
−==
ρ (39)
In the new Moens-Korteweg equation, the expression h/Ri is replaced by (2R-
i-h)/2(Ri-h), the new BP-PTT relationship equation can be deduced from Eq. (30) by
directly replacing h/Ri with (2Ri-h)/2(Ri-h), thus only the value of parameter ∆Z/pwv0
is different in this BP-PTT relationship after considering the effect of vessel radius,
actually the calculated value of parameter ∆Z/pwv0 has already includes the effect of
vessel radius.
The obtained average PWV value is in the range from 7ms/s to 15m/s with no
external contact force, the increase in PWV toward the periphery has been confirmed
by a number of studies (Milnor, 1989; Hoeks et al., 1999). In peripheral artery, PWV
can reach 15m/s (Rourke and Brands, 1999). Theoretically, blood flow velocity must
be subtracted from the calculated or measured PWV to obtain the true PWV. However,
since the blood flow velocity is of the order of 0.25m/s compared to 5m/s for the
PWV (Posey, 1972), this correction was not made in the comparison of theoretical
results and the measured ones.
59
PTT was measured from the ECG QRS complex rather than from aortic valve
opening, and therefore included a contribution from the left ventricular isometric
contraction time (PEP). It was found that PEP is sensitive to the sympathetic nerve
system rather than to BP. Some beta-adrenergic blocking agents, such as isoproterenol,
epinephrine and amylk nitrite, reduce the isovolumetric contraction time
(Wippermann et al., 1995). The PEP at rest can be normally treated as a constant,
other studies have reported that PEP is roughly 69+ 5ms (Payne et al., 2006), but
there is not yet a mature method to noninvasive measure PEP value. The inaccuracy
for BP estimation caused by PEP is inevitable.
6.2 INFLUENCE FACTORS TO PRECISION IN PROPOSED CALIBRATION
METHOD
Chapter 5 has theoretically deduced calibration method and set up its corresponding
procedure, but there are several operations may affect the accuracy during calibration
for the BP-PTT relationship. Firstly, calibration method requires user to measure one
time SBP & DBP and PTT without external cuff pressure on arm arterial at the same
time. However, this is impossible since using auscultation method or others to
measure one time SBP & DBP adds external pressure on the arm arterial. To approach
simultaneously measurement, the calibration procedure is designed to firstly record
PTT value, then start to measure one time SBP & DBP using Medical oscillometric
Sphygmomanometer EW3152, the time difference between these two procedures is
set to be as short as possible. During the measurement, the height level of
measurement hand is required to be the same as that of heart. After that, user is
instructed to measure their PTT value for different external cuff pressure. The added
external cuff pressure is not exactly the same as designed due to air leakage. Thus, a
high quality cuff which has an auto-inflation valve can keep the pressure inside cuff
be the same as designed. Due to time limited, designed system doesn’t integrate such
a valve into cuff, only use mercury sphygmomanometer to manually add certain cuff
pressure on arm arterial which may cause inaccuracy in the calibration. To tackle this
60
problem, calibration procedure is designed to add several different cuff pressures to
calculate the average value of parameters inside BP-PTT relationship equation.
CHAPTER 7: TEST RESULTS AND ANALYSIS
7.1 CALIBRATION AND MAP MEASUREMENT PROCEDURE
As shown on Fig. 34, the constructed hardware circuit mainly includes three modules:
SPG & ECG signal conditioning circuit and two-channel signal acquisition. The SPG
and ECG signal are firstly sampled by biomedical sensors and separately sent into
signal conditioning circuit, which processes ECG & SPG signal as ones within ADC
required range. After that, the analog SPG & ECG are digitized in two-channel signal
acquisition modules and combined together; extra-codes are added into signals and
further transferred into computer through USB cable.
Two-Channel Signal Acquisition
SPG Signal Conditioning Circuit
ECG Signal Conditioning Circuit
ECG Sensor
SPG Sensor
Figure 34. Hardware of prototyping system
As shown on Fig. 35, constructed interface continuously record and display measured
SPG & ECG waveform for 4.5 seconds. After that, interface displays calculated
beat-to-beat PTT & BP value within 4.5 seconds and refresh the screen. The region in
interface marked as A is the user instruction which guides home user follow
62
calibration procedure to get BP-PTT relationship equation individually, after
calibration the parameters of user’s BP-PTT relationship is recorded and can be reload
for further BP measurement. The region in interface marked as B calculates average
PTT & BP value, shows out their range within each 4.5 seconds.
Figure 35. Interface of prototyping system on PC
7.2 TESTING OF EXTERNAL PRESSURE BASED CALIBRATION METHOD
The prototyping system of real-time cuffless BP estimation system is constructed and
tested. Previous research on 24 hours dynamics BP monitoring indicates that both
normotensive and hypertensive have the BP day night rhythm: BP becomes the lowest
points during midnight from 0 to 3 clock, starts to increase after wake up in the
morning and reach peak point at about 8-9 am. BP remains high level in the day time
and reaches peak point again at about 5-6 pm, after that its value declines. BP’s value
becomes low level in the evening, its dynamics range is about 20-30mmHg. BP in a
short time changes frequently due to the effect of many factors such as tester’s
respiration, emotion and hydrostatic pressure effect, but its value remains in a certain
63
region. In order to completely know how accuracy this system can achieve and
whether it can successfully track the tendency of BP change, how long the calibrated
parameters can be used to accuracy measure BP value is also estimated. Thus, testing
has been divided into two groups: 1) continuously test people with different ages
ranging from 24 to 66 for three time phase on a day as shown in Table 5; 2) 2nd testing
group is separated into two parts as shown in Table 6: first part monitors same person
on different days in the 1st month, second part tests same people with same calibrated
parameters two months later, four testers with different ages ranging from 23 to 64 are
selected to take this testing. Adopting proposed calibration method, testers are all
selected to obtain their calibration curves on Aug. 18, 2011. The test results by
designed system are compared by Medical Oscillometric Sphygmomanometer
EW3152.
It is observed from Table 5 that the average/mean accuracy for MAP estimation by
designed system is 95.87%, the standard deviation (SD) for the accuracy of MAP
estimation is 1.06% and the mean system error between designed system and Medical
Oscillometric Sphygmomanometer is 2.84mmHg. SD is a widely used measurement
of variability or diversity used in statistics and probability theory which shows how
much variation or “dispersion” there is from the average (mean, or expected value). A
low standard deviation indicates that the data points tend to be very close to the mean,
whereas high standard deviation indicates that the data are spread out over a large
range of values, Table 5 calculates SD as 1.06%. Both the accuracy and SD indicate
that designed system can be adopted to estimate the MAP at about 95% accuracy
without age limit in a day.
Fig. 36 graphs the testing results for each testers in 2nd testing group, it clearly show
that designed system can successfully track the MAP changing tendency within three
months. It is observed from Table 6 that the average/mean accuracy of MAP
estimation in 3rd month is 96.34% which is almost the same as the accuracy in 1
st
month (96.59%), this indicates that designed system can still accurately estimate
MAP after a long time period, this result is consistent with the accuracy analysis in
64
previous chapter. The average accuracy of MAP in 2nd testing group is 96.54%, the
SD value is calculated as 1.06%.
Table 5. Monitoring MAP results sampled from different testers on a day
Tester
No.
Tester’s
age
Record
Time
PTT
(ms)
Average
MAP3
(measured by
constructed
system )
Average MAP
(measured by
Oscillometric)
System
Accuracy
1 24 2011/10/20
11:24 am 154 83mmHg 86mmHg 96.51%
2011/10/20
03:08 pm 159 80mmHg 83mmHg 96.39%
2011/10/20
08:10 pm 147 89mmHg 93mmHg 95.70%
2 23 2011/10/21
12:06 am 158 81mmHg 85mmHg 95.29%
2011/10/21
03:00 pm 170 69mmHg 65mmHg 93.85%
2011/10/21
08:00 pm 161 76mmHg 79mmHg 96.20%
3 33 2011/10/24
10:50 am 120 89mmHg 92mmHg 96.74%
2011/10/24
03:36 pm 117 90mmHg 94mmHg 95.74%
2011/10/24
07:00 pm 150 80mmHg 76mmHg 94.74%
4 31 2011/10/20
11:20 am 174 63mmHg 67mmHg 94.03%
2011/10/20
03:24 pm 154 76mmHg 79mmHg 96.20%
2011/10/20
06:00 pm 172 63mmHg 65mmHg 96.92%
5 42 2011/10/20
11:00 am 144 75mmHg 79mmHg 94.94%
2011/10/20
03:36 pm 142 76mmHg 79mmHg 96.20%
2011/10/20
07:00 pm 143 76mmHg 80mmHg 95.00%
6 65 2011/10/21
11:30 am 125 103mmHg 105mmHg 98.10%
2011/10/21
03:40 pm 130 98mmHg 95mmHg 96.84%
2011/10/21 124 103mmHg 100mmHg 97.00%
65
18:37 pm
7 66 2011/10/21
10:40 am 123 87mmHg 84mmHg 96.43%
2011/10/21
03:21 pm 135 76mmHg 79mmHg 96.20%
2011/10/21
06:00 pm 118 93mmHg 88mmHg 94.32%
Mean
Accuracy 95.87%
Standard
Deviation1
1.06%
System
Mean
Error2
(mmHg)
3.38
1 Standard deviation measures the variety of the measurement accuracy
2 System Mean errors calculates the average value of difference between measured MAP by designed system and
that by Medical Oscillometric Sphygmomanometer EW3152
3 The average MAP is estimated by calculating the mean value for continuously 60 seconds’ beat-to-beat MAP
value measured by constructed system
Table 6. Monitoring MAP results sampled from different testers on different days
within a month and two months later testing same person with previous calibrated
parameters
Tester
No.
Tester’s
age
Record
Time
PTT
(ms)
Average
MAP5
(measured by
constructed
system )
Average MAP
(measured by
Oscillometric)
Accuracy
1 23 2011/08/18
11:49 am 139 96mmHg 93mmHg 96.77%
2011/08/19
06:35 pm 132 99mmHg 104mmHg 95.19%
2011/08/20
00:45 am 113 101mmHg 105mmHg 96.19%
2011/08/22
00:46 pm 132 92mmHg 96mmHg 95.83%
2011/08/24
03:13 pm 134 91mmHg 93mmHg 97.85%
2011/10/21
03:40 pm 140 87mmHg 89mmHg 97.75%
2011/10/22 149 81mmHg 84mmHg 96.43%
66
05:25 am
2011/10/24
04:40 pm 140 87mmHg 91mmHg 95.60%
2 32 2011/08/18
03:44 pm 131 60mmHg 64mmHg 93.75%
2011/08/19
06:00 pm 148 54mmHg 57mmHg 94.74%
2011/08/22
05:09 pm 125 63mmHg 67mmHg 94.03%
2011/08/23
03:10 pm 150 54mmHg 52mmHg 96.15%
2011/08/24
02:45 pm 135 57mmHg 59mmHg 96.61%
2011/10/20
00:20 pm 154 52mmHg 55mmHg 94.55%
2011/10/21
04:24 pm 152 53mmHg 54mmHg 98.15%
2011/10/24
03:03 pm 152 53mmHg 56mmHg 94.64%
3 40 2011/08/18
04:16 pm 112 82mmHg 86mmHg 95.35%
2011/08/19
02:07 pm 108 86mmHg 89mmHg 96.63%
2011/08/20
09:20 am 95 98mmHg 100mmHg 98.00%
2011/08/22
03:00 pm 110 83mmHg 79mmHg 94.94%
2011/08/23
06:00 pm 115 80mmHg 77mmHg 96.10%
2011/10/21
03:50 pm 120 77mmHg 73mmHg 94.52%
2011/10/24
10:50 am 120 77mmHg 79mmHg 97.47%
2011/10/25
01:36 pm 117 74mmHg 76mmHg 97.37%
4 64 2011/08/19
06:32 pm 109 92mmHg 96mmHg 95.83%
2011/08/20
05:46 pm 100 98mmHg 100mmHg 98.00%
2011/08/21
04:30 pm 105 95mmHg 97mmHg 97.94%
2011/08/22
05:55 pm 105 95mmHg 97mmHg 97.94%
2011/08/24
03:40 pm 104 95mmHg 94mmHg 98.94%
2011/10/20 105 95mmHg 96mmHg 98.96%
67
04:49 pm
2011/10/21
04:40 pm 110 92mmHg 95mmHg 96.84%
2011/10/26
04:47 pm 108 92mmHg 95mmHg 96.84%
(1st Month)
Mean
Accuracy1
96.34%
(3rd Month)
Mean
Accuracy2
96.59%
Mean
Accuracy 96.54%
Standard
Deviation3
1.419%
System
Mean Error4
(mmHg)
2.84
1 (1st Month) mean accuracy represents average MAP measurement accuracy of designed system in 1st month’s
testing
2 (3rd Month) mean accuracy represents average MAP measurement accuracy of designed system in the testing
after two months
3 Standard deviation measures the variety of the measurement accuracy
4 System mean error calculates the average difference between measured MAP by designed system and that by
Medical Oscillometric Sphygmomanometer EW3152
5 The average MAP is estimated by calculating the mean value for continuously 60 seconds’ beat-to-beat MAP
value measured by constructed system
404040405050505060606060707070708080808090909090100100100100110110110110
1111 2222 3333 4444 5555 6666 7777 8888Number of TimesNumber of TimesNumber of TimesNumber of TimesMeasured BP (mmHg)Measured BP (mmHg)Measured BP (mmHg)Measured BP (mmHg) Tester No.1 (S)Tester No.1 (S)Tester No.1 (S)Tester No.1 (S)Tester No.1 (O)Tester No.1 (O)Tester No.1 (O)Tester No.1 (O)Tester No.2 (S)Tester No.2 (S)Tester No.2 (S)Tester No.2 (S)Tester No.2 (O)Tester No.2 (O)Tester No.2 (O)Tester No.2 (O)Tester No.3 (S)Tester No.3 (S)Tester No.3 (S)Tester No.3 (S)Tester No.3 (O)Tester No.3 (O)Tester No.3 (O)Tester No.3 (O)Tester No.4 (S)Tester No.4 (S)Tester No.4 (S)Tester No.4 (S)Tester No.4 (O)Tester No.4 (O)Tester No.4 (O)Tester No.4 (O)
68
Figure 36. Graph of monitoring MAP results sampled from different testers on
different days within three months (symbol “S” & “O” indicates MAP results
measured by designed system and by Oscillometric separately)
7.3 TESTING OF ADAPTIVE HYDROSTATIC CALIBRATION METHOD
Research teams have proposed different calibration methods for BP-PTT relationship
from 1996 till now, such as motion based calibration method, hydrostatic pressure
based calibration method and model based calibration method. Many researchers
selects hydrostatic pressure based calibration method due to its easy operation
comparing with others. Thus, this method is also applied into our system to test its
MAP estimation accuracy which is further compared with proposed external pressure
based calibration method. Seven volunteers were recruited to do the testing, MAP &
PTT were measured simultaneously while subjects are instructed to raise their right
hands such that their wrists are 0-60cm above heart level in a randomized order of
steps of 15cm. Subjects were asked to maintain each position for 15 seconds while
ECG & PPG were recorded. By putting recorded four groups of MAP & PTT value
into Eq. (40), the average value of parameters a & b in the linear relationship between
BP and PTT can be calculated. The calibration procedure is the same as that proposed
by Carmen in CUHK (Carmen, 2006).
PTTbaMAP ×+= (40)
In order to know the MAP estimation accuracy using hydrostatic pressure based
calibration method, system is constructed to test people with ages from 24 to 66 at
three time phase on a day. The test results are compared with the mean value of three
times MAP measurement by Medical Oscillometric Sphygmomanometer EW3152 as
shown on Table 7, the time interval for each measurement is about 15 minutes.
From Table 7 can see that the average accuracy using hydrostatic pressure based
calibration method is 95.17%, SD is calculated as 1.67% and the system mean error is
3.86mmHg. Table 8 compares the accuracy for both calibration methods, by using
external pressure based calibration method the testing accuracy for MAP estimation is
higher and SD & mean system error is smaller.
69
Table 7. Monitoring MAP results sampled from different testers on a day
Tester
No.
Tester’s
age
Record
Time
Average
MAP3
(measured by
constructed
system )
Average MAP
(measured by
Oscillometric)
System
Accuracy
1 19 2009/05/07
10:00 am 96mmHg 101mmHg 95.05%
2009/05/07
03:00 pm 90mmHg 95mmHg 94.74%
2009/05/07
08:00 pm 97mmHg 100mmHg 97.00%
2 22 2009/05/07
10:00 am 82mmHg 86mmHg 95.35%
2009/05/07
03:00 pm 77mmHg 79mmHg 97.47%
2009/05/07
08:00 pm 80mmHg 84mmHg 95.24%
3 30 2009/05/07
10:00 am 89mmHg 84mmHg 94.05%
2009/05/07
03:00 pm 76mmHg 80mmHg 95.00%
2009/05/07
07:00 pm 85mmHg 88mmHg 96.59%
4 30 2009/05/08
11:00 am 57mmHg 63mmHg 90.48%
2009/05/08
04:00 pm 48mmHg 50mmHg 96.00%
2009/05/08
07:00 pm 52mmHg 55mmHg 94.55%
5 40 2009/05/08
11:00 am 75mmHg 80mmHg 93.75%
2009/05/08
04:00 pm 74mmHg 79mmHg 93.67%
2009/05/08
07:00 pm 85mmHg 88mmHg 96.59%
6 40 2009/05/08
11:00 am 72mmHg 77mmHg 93.51%
2009/05/08
04:00 pm 65mmHg 68mmHg 95.59%
2009/05/08
07:00 pm 70mmHg 75mmHg 93.33%
7 63 2009/05/09
11:00 am 93mmHg 95mmHg 97.89%
70
2009/05/09
04:00 pm 88mmHg 91mmHg 96.70%
2009/05/09
07:00 pm 95mmHg 99mmHg 95.96%
Mean
Accuracy 95.17%
Standard
Deviation1
1.67%
System
Mean
Error2
(mmHg)
3.86
1 Standard deviation measures the variety of the measurement accuracy
2 System Mean errors calculates the average value of difference between measured MAP by designed system and
that by Medical Oscillometric Sphygmomanometer EW3152
3 The average MAP is estimated by calculating the mean value for continuously 60 seconds’ beat-to-beat MAP
value measured by constructed system
Table 8. Comparison of testing accuracy for MAP estimation using both calibration
methods
Calibration Method Mean Accuracy Standard
Deviation
System Mean
Error (mmHg)
External Pressure
Based Calibration
Method
95.87% 1.06% 3.38
Hydrostatic
Pressure Based
Calibration Method
95.17% 1.67% 3.86
71
7.4 COMPARISON AND ANALYSIS ON TESTING RESULTS
The testing result shows that system adopting external pressure based calibration
method has a MAP estimation accuracy which is comparable with hydrostatic
pressure based calibration method. Most subjects in the testing feedback that holding
elevated hand position for 15 seconds and simultaneously measure MAP by
Oscillometric causes hand ache, produces hand vibration which may affects the
accuracy of detecting the coefficient factor in BP-PTT relationship. Whereas external
pressure based calibration method only need to add three different external pressures
which are smaller than the MAP value and doesn’t need to simultaneously measure
MAP and PTT, thus it doesn’t bring uncomfortable during calibration procedure. The
experiment data indicates that the total time for hydrostatic pressure based calibration
method is longer than external pressure based calibration method. This is because the
former one requires subjects to hold elevated hand position for 15 seconds in each
step and measuring MAP by Oscillometric needs three minutes.
Through above comparison, it can conclude that external pressure based calibration
method uses less time for calibrating BP-PTT relationship with more comfortable
procedure; its accuracy for MAP estimation is comparable with that of hydrostatic
pressure based method.
CHAPTER 8: CONCLUSION AND FUTURE WORK
The thoroughly survey after reading hundred papers or academic materials helps me
fully understand the diverse advanced technologies, past research achievements,
existing bottle-neck problems, challenges and main difficulties in captioned research
topic. From chapters expounded above, there are mainly three bottleneck problems in
real-time BP monitoring by using PTT based method for e-home healthcare. My
thesis research work overcomes obstacles and successfully constructs a real-time
cuffless MAP estimation system which is summarized as follows:
1) Propose an automatic SPG sampling scheme with signal conditioning circuit and
relevant software for realizing signal amplitude & baseline-shift self-adjustment.
Due to existing external disturbance during SPG acquisition, a close-loop control
is constructed between computer and MCU based on the principle of E. I. D. C.,
so that to acquire the self-adjusted stable SPG signal fast with less distortion.
2) PTT is defined as time interval from the peak of ECG R-wave to the onset point
of pulse wave on periphery arterial, thus SPG &ECG waveforms are separately
transferred into feature point detection to find out their peak points. To approach
real-time feature point detection, SPG and ECG waveforms are collected to take
feature point detection each few seconds. Due to existing the feature points
mis-detection and possible loss of relative SPG or ECG waveforms within that
few seconds, a real-time PTT estimation scheme with several rules defined to
detect adjacent peak points of ECG & SPG but from different pulses is
constructed, such that to reduce PTT calculation error.
3) Design an external pressure based calibration method by using external cuff
pressure on arm arterial to find out parameters in BP-PTT relationship. In 2007,
Teng et al.’s research in CUHK demonstrated that PTT increased with the contact
pressure on the fingertip up to approximate zero transmural pressure and
maintained a near constant level in the test range of contact pressure. Based on
73
that, a calibration method which uses three groups of external cuff pressure on
arm arterial is developed to get the coefficient value in BP-PTT relationship. The
further testing result indicates that its operation procedure is easy and comfort
which is comparable with previous research and suitable for e-home healthcare.
4) Explore how much the relevant factors affect the accuracy of BP estimation,
firstly the conditions for establishment of BP-PTT relationship is investigated,
such as the thickness of vessel wall is constant, vessel radius are much larger than
thickness of vessel wall and blood is a kind of incompressible fluid etc. Recent
research find out the actually measured PTT as the time interval from the
characteristic points of ECG and PPG signal in the same cycle contains PEP
when the ventricular contraction occurs and the semilunar valves open and blood
ejection into the aorta commences, thus PEP is studied for PTT accuracy. Third,
the errors issuable in calibration are analyzed, such as hydrostatic pressure effect
and so on. By this exploration, pave a feasible way like modification on
calibration method/procedure to improve the accuracy of MAP estimation.
Finally, the prototyping system is constructed and tested. The testing assesses the
accuracy for MAP measurement by using external pressure based calibration method
and hydrostatic pressure based calibration method separately, testing result and
comparison indicate that system adapting previous method uses less time and more
comfortable procedure to calibrate BP-PTT relationship; its MAP estimation accuracy
is comparable with that of previous one.
By combining Bluetooth communication technology with the SPG & ECG sampling
scheme and designing a watch-type measurement device instead of using elastic band
to attach piezoelectric transducer on wrist in the future, the scheme can offer better
solution to cardiovascular monitoring and diagnosis system in e-home healthcare.
Currently, the calibration steps of one time MAP measurement by Oscillometric and
adding external pressures are separated, these two steps can be further combined
together by embedding a controllable air inflation cuff into designed system, such that
74
to make the calibration procedure easier. Future works also include more system
testing, such as testing on sick people with CVDs.
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force on pulse transit time.” In IEEE Transactions on Biomedical Engineering,
vol. 54, no. 8, pp. 1490-1498.
Teng, X. and Zhang, Y., 2005. “Continuous and noninvasive estimation of arterial
blood pressure using photoplethysmographic approach.” In Proceeding of the 25th
Annual International Conference of the IEEE Engineering in Medicine and
Biology Society.
Zheng, L., Sun, Z. and Li, J., 2008. “Pulse pressure and mean arterial pressure in
relation to ischemic stroke among patients with uncontrolled hypertension in rural
areas of China.” In Stroke, vol. 39, no. 7, pp. 1932-7.
APPENDIX A: PUBLICATIONS
JOURNAL PAPERS (3):
1. FANG Weixuan, DOU Jiayi, HU Xiangyang, DONG Ming Chui, LEI WaiKei,
"Cuffless blood pressure acquisition system based on a novel calibration method",
Chinese Journal of Medical Instrumentation, vol. 35(1): 6-10, May 2011.
2. FANG Weixuan, DONG Mingchui, LEI Waikei, HU Xiangyang, “An Approach
Towards Intelligent Sphygmogram Sampling for e-Home Healthcare”, Computer
Methods and Programs in Biomedicine, Submitted on 23th of June 2011.
3. FANG Weixuan, DONG Mingchui, LEI Waikei, HU Xiangyang, “Automatic
Pulse Wave Fast Sampling in e-Home Healthcare Utilizing Close-loop Control”,
IEEE Transactions on Information Technology in Biomedicine, Submitted on 29th
of October 2011.
CONFERENCE PAPERS (3):
1. FANG Weixuan, DONG Mingchui, LEI Waikei, "Novel system sampling multi
vital signs for e-Home healthcare". 7th International Conference on Information,
Communications and Signal Processing (ICICS 2009), Macau, China, pp.1-5, Dec.
2009.
2. FANG Weixuan, DONG Mingchui, LEI Waikei, HU Xiangyang, "A Novel
Sphygmogram Sampling and Self-adjusting Scheme for e-Home Healthcare",
2011 International Conference on Embedded Systems and Applications
(ESA'2011), Las Vegas, Nevada, USA, pp.10-14, July 2011.
3. FANG Weixuan, DONG Mingchui, LEI Waikei, HU Xiangyang, "An Approach
Towards Cuffless Blood Pressure Estimation for e-Home Healthcare",
International Conference on Bio-inspired Systems and Signal Processing
80
(BIOSIGNALS 2012), Vilamoura-Algarve Portugal, Feb. 2012, Accepted on 19th
of October 2011.
APPENDIX B: PROTOTYPING SYSTEM
ONE PROTOTYPING SYSTEM:
This research constructs a prototyping system called as Real-time Cuffless MAP
Estimation System.
Figure 37. Hardware of real-time cuffless MAP estimation system
82
Figure 38. Interface of real-time cuffless MAP estimation system
VITA
Fang Wei Xuan
University of Macau
2011
Mr. Fang Wei Xuan was educated in Department of Electrical and Electronics
Engineering, Faculty of Science and Technology (FST), University of Macau (UM)
from Sep. 2005 to in Jul. 2009. Since Sep. 2009 Mr. Fang started his master program
in the Department of Electrical and Computer Engineering in UM. With special
interest in biomedical engineering, he joined 3 R&D projects and owns around 4 years
of research experience on signal acquisition, processing and analysis. His research
focuses on prognosis of cardiovascular diseases (CVD) as well as on cuff-less blood
pressure measurement based on pulse transit time (PTT) based method. So far, he has
published two conference papers and one Chinese journal paper, recently got one
conference paper accepted and submitted two international journal papers.
