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CERT IN INVESTIG TIONS ON
ELECTROM GNETIC INTERFERENCE
IN ECG
Presented by
ARUN B THAHARoll no:T403M.Tech(CommunicationSystems)TKMCE
Guided byDr. UNNI C.
Professor
Dept of ECE
TKMCE
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INTRODUCTION Electromagnetic interference (EMI) is a function of power output and frequency of
transmitting device.
EMI is a self-propagating wave in space with electric and magnetic components.
These components oscillate at right angles to each other and to the direction ofpropagation.
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EM
Radio Waves, MicroWaves, tetra hertz
radiation
Infra red, Visiblelight, UV radiation,
X& Gamma rays
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The signal voltage of ECG is in milli volt range. Therefore it is very easy to be
influent by the EM environment.
Exponential cellular phone result in level of EM Radiation.
Use of such devices will the productivity but the performance of othermedical equipments working in its vicinity.
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PROBLEM STATEMENT In this automated era, ECG devices had undergone developments and
advances in its application.
However, the problem of EMI still exists in ECG signal which will adverselyaffect the accuracy of ECG measurement.
The error thus occurred will lead to a delay or inappropriate treatmentdecision made by the doctor.
This might be a severe problem for those patients who are seriously injuredor to the patients in emergency.
So we should find effective mechanisms to avoid this impact.
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OBJECTIVES To study the environment based condition of EMI during ECG acquisition
To study various noises present in the ECG signals
To study Stationary Wavelet Transform (SWT), Wavelet Filtering (WF), WaveletWiener Filtering (WWF) and Adaptive Wavelet Wiener Filtering (AWWF).
To suppress interferences present in ECG signal
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LITERATURE SURVEY
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Works by Stuchlybrothers and Foster on Dielectric properties oftissues and biological materials in 1980s
Impact of Electromagnetic Interference on Critical MedicalEquipments by RF Devices
Work by Hong Ming and co authors in 2006 Work by Janusek and co authors in 2013
Commercially available simulation tools Works on ECG Signal processing
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EXPERIMENT To investigate interference (EMI) of electromagnetic fields from GSM mobile
phones several numerical models of human body from the database of NEVA
Electro-magnetics is used.
This study uses a homogeneous human model with permittivity 50 and conductivity0.5 S/m.
The possibility of charge accumulation by exposing the human body to an electric
field while using GSM mobile phones within in a couple of centimeters is studiedwith the E42 software package
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E42 PACKAGE
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From the above study it is well clear that the amount of charge accumulated on thehuman body also depend on the posture for a given electric field.
The impact of surface charge accumulation on the human body during the ECGsignal acquisition is a great matter of concern as ECG leads may pick this potential
along with the actual signal resulting in inaccurate diagnosis.
The issue is studied with the help of EM field Solver
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EM SOLVER
The basic NEVA EM Solver is robust, fast, and comes with many unique and
detailed human body models. The Solver's available customization is also much
more cost-effective in comparison to most other offerings.
Some Highlights of EM solver are
Small radiation box.
Simulation Environment
Speed
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OBSERVATIONS
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From the above experiments it is clear that the ECG signals might be corrupted byGSM mobile phones working in its vicinity.
It should be noted that even if the mobile phone is not used for any voice or datacommunication it is periodically transmitting low frequency bursts in DTX mode.
The noise level in DTX mode is lower but its characteristics may cause problems asits presence in ECG signal may mimic fibrillation events, makes the situation more
worst.
So we should find out an efficient mechanism to eliminate such interfering signalfrequencies especially at the ELF and LF band signals emitted during DTX mode.
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WAVELET BASED ADAPTIVE NOISECANCELLATIONDATA SETS
Data base employed to asses the performance of this method is obtained from
MIT-BIH database
The artificial noise used are generated individually for each signal
respecting the original noise level and its time dependence.
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SWT SWT is a modified version WT
Important parameters- decomposition level, impulse characteristics of initial LPF &HPF.
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WAVELET FILTERING Based on appropriate adjustment of wavelet coefficients
On the basis of ECG wavelet coefficients it is easy to separate the interference &signal via thresholding
Effective thresholding requires
Evaluate right value of threshold
To choose right thresholding method
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THRESHOLD LEVEL
x(n) is the noisy signal
x(n) =s(n) + w(n); n=0,1N-1
N length of signalo On transforming using dyadic SWT
ym(n) = um(n) + vm(n)
Threshold level for modifying coefficients is set on the basisof noise level vm.
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Threshold value is optimized by
If level is too low- Occurrence of noise
If level is too high- damage the noise free signal
Adaptivity in threshold level selection is achieved by
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WAVELET WIENER FILTERING
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HW is Wiener correction factor
Using HW modified coefficients are obtained
Y(n) is obtained by taking inverse SWT2
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ADAPTIVE WAVELET WIENERFILTERING
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WWF has many parameters that has to be set manually
Appropriate setting of these parameters has great influence on thefiltering results.
Its not clear that which parameter is suitable for ECG
Parameters vary on noise levels
The robust filtering algorithm should change the parameters on the basisof noise level
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SETTING PARAMETERS
3 important parameters are taken into consideration here
Level of decomposition
Filter banks
Thresholding methods
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EVALUATION CRITERIA
SNR improvement
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SYNTHETIC NOISE Most of the authors used white Gaussian noise as artificial
interference
This paper focuses on removing interferences in the LF range.
Therefore noise having power spectrum similar to powerspectrum of interference is created.
Noise = Sin(2*pi*f*length of input)+
SNRin*random(length of input)
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AWWF ALGORITHM Initialize the necessary parameters
Set SNRin
Noise = Sin(2*pi*f*length of input)+SNRin*random(length of input)
Noisy ECG = i/p ECG+Noise
Set parameters for WWF(threshold level, threshold type, filter
bank,) Perform SWT on noisy ECG
Compute Noise estimate using noisy signal and above o/p.
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Use parameters above discussed to perform AWWF
Iterate through each parameters in loops to obtain optimumvalues and hence the noise estimate.
Compute the best SNR estimate from stored noise estimate
Filter the ECG signal using the above results.
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SIMULATION RESULT
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CONCLUSIONS The study shows the worst case level of the interference
induced in ECG electrodes.
Intensity of interference depends on the position of the mobilephone.
The observations from the simulated study shows that duringECG acquisition mobile phones should be either switched off
or it should be located at a distance greater than 15 cm from
any of the ECG electrodes to prevent artifacts in the
electrocardiographic signal.
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The proposed algorithm provides better filtering results than otheralgorithm based on WWF algorithm for ELF and LF ranges.
The new algorithm is adaptive and adaptation lies in the division of signalinto individual segments each with constant level of noise.
These segments are filtered using parameters appropriate for given noiselevel.
Thus its evident that the filter can deal with dynamically changingnoise.
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REFERENCES1) Stuchly, M. A., and S. S. Stuchly, Dielectric Properties of Biological
SubstancesTabulated, J. of Microwave Power, Vol. 15, No. 1, 1980,
pp. 1926.
2) Foster, K. R., and H. P. Schwan, Dielectric Properties of Tissues and
Biological Materials: A Critical Review,Critical Reviews in Biomedical
Engineering, Vol. 17, No, 1, 1989, pp. 25104.
3) Duck, F. A., Physical Properties of Tissue: A Comprehensive Reference
Book, New York: Academic Press, Harcourt Brace Jovanovich, 1990.
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4) Hong ming, Yajun zhang and Weijiang Pan, Evaluation and Removal of
EMI between ECG Monitor and GSM Mobile Phones,ICWMMN 2006
Proceedings.
5) T. Buczkowski, D. Janusek, H. Zavala-Fernandez, M. Skrok, M. Kania,A. Liebert, Influence of Mobile Phones on the Quality of ECG Signal
Acquired by Medical Devices, Measurement Science Review, Vol.13,
No. 5, 2013.
6) P. M. Agante and J. P.Marques de Sa, ECG noise filtering usingwavelets with soft-thresholding methods, Comput. Cardiol., vol. 26, pp.
535538, Sep. 1999. DOI: 10.1109/CIC.1999.826026.
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THANK YOU