Non-invasive blood pressure measurements: Separation of the arterial pressure

oscillometric waveform from the deflation using digital filtering

Vojko Jazbinšek, Janko Lužnik, Zvonko Trontelj

Institute of Mathematics, Physics and Mechanics, University of Ljubljana, Ljubljana

Jadranska 19, 1000 Ljubljana, Slovenijavojko.jazbinsek@imfm.uni-lj.si

Introduction• Many oscillometric non-invasive blood pressure

(NIBP) measuring devices are based on recording the arterial pressure pulsation in an inflated cuff wrapped around a limb during the cuff deflation [1].

• The recorded NIBP data contain the arterial pressure pulses in the cuff, called oscillometric pulses, superimposed on the cuff deflation.

• Some of NIBP devices have implanted microphone inside the cuff, which enables measurements of Korotkoff sounds [2]. These sounds are used in a conventional auscultatory method (stethoscope).

1 K-G. Ng, CF .Small. Survey of automated non-invasive blood pressure monitors, Journal of Clinical Engineering; 19:452-475, 1994.

2 N.C. Korotkoff. On the subject of methods of determining blood pressure. Bull. Imperial Mil. Med. Acad. (St. Petersburg), 11:365-367, 1905.

• We have applied the Fourier analysis and digital filtering:– to separate the cuff deflation from the

arterial pressure pulses– to extract the Korotkoff sounds from the

microphone data – to estimate the arterial pressure pulses from

the microphone data

Summary

Measurements• EU-project “Simulator for NIBP”

• Compressor for the cuff inflation and a sensor for pressure detection, built in a PC (LODE, NL)

• Upper arm cuff (Accoson, UK)

• Piezoceramic microphone with a diameter of 4 cm.

• Commercial automated NIBP device OSZ 4 (Welch Allyn, USA).

• Two modes of cuff deflation:

a) Linear (feedback loop in PC)

b) Exponential (Valve)

Measured signals

Pressure

Microphone

• Segmentation of data (red) into pulses (blue vert. lines)

• The deflation signal is calculated by the interpolation of data between subsequent segment borders (green)

• By subtraction of deflation from the measured data we obtained so called oscillometric pulses (black) with only positive deflections

Part I: Pressure data

Frequency spectrum of measured pressure data

Digital filtering:• FFT frequency spectrum

• (frequency spectrum) × (filter)

• FFT-1 to the time space

Butterworth filter of 6th order

Asymmetrical extended data to 218 points

Filtered (0.3-20 Hz) asymmetrical extended data

first last

Arterial pressure pulses:

Frequency spectrum of microphone data

Part II: Microphone data

Filtered Korotkoff (0.3-10 Hz)

Time derivative of pressure data

t

ttpttpt

dt

dp iii

2

)()()(

)()(2)( ttFtKtttF iii

0)( ttF

Derivative:

Antiderivative:

OSZ4: a) 117/78/60 b) 127/83/60 (systolic/diastolic pressure/heart rate)

A – from measured pressure in the cuff

B – from low-frequency part of microphone data

a) PC

b) Valve

WAVEFORM (norm. pulse amplitude vs. deflation)

117

127

83

78

Diastolic and systolic pressures

• height-based approach: the ratio between the waveform (envelope) value at a given pressure and the maximum value is used

Two general types of criteria have been used in automated NIBP devices to determine systolic and diastolic blood pressure values from oscillometric waveforms [1]:

height

slope

• slope-based method: the slope of waveform curve (rise and fall) is used.

These criteria are all empirically derived and different manufactures use different criteria [1].

waveform

1 K-G. Ng, CF .Small. Survey of automated non-invasive blood pressure monitors, Journal of Clinical Engineering; 19:452-475, 1994.

Audible part of microphone data

OSZ4: a) 117/78/60 b) 127/83/60 (systolic/diastolic pressure/heart rate)

117

12783

78

Conclusions (I. part)• Digital Fourier filtering could be used for analysis of

measured signals in NIBP devices.

• Deflation could be separated from the arterial pressure pulses by means of digital filtering without segmentation of measured data into pulses.

• Furthermore, the segmentation borders coincide with the time points that determine the negative envelope of filtered pulses.

• The waveform describing the pulse amplitudes as a function of the deflation is a base for automated determination of the systolic and diastolic pressures.

Conclusions (II. part)

• With the frequency analysis of microphone data we showed that those signals could be divided into two parts,

• The low frequency part (0.3-10 Hz) is approximately equivalent to the time derivative of pressure changes in the cuff.

• Using iterative numerical antiderivative procedure, typical shape of oscillometric waveform can be reconstructed.

• The higher frequency, audible part of the microphone data can be directly connected to the conventional auscultatory method.

• The shape of this signal can be also used for automated determination of the systolic and diastolic pressure values.

Waveforms: A – pressure data, B – low frequency part and C – audible part of microphone data

AB C

Poster (ID 2268): AVERAGING OSCILLOMETRIC NON-INVASIVE BLOOD PRESSURE RECORDINGS: TRANSFORMATION INTO THE NORMALISED VIEW

Measurements with external artefacts:

Thank you for your attention