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● Introduction ● Method ● Results ● Conclusion

The International Symposium on Emerging Areas in Biotechnology & Bioengineering (ISEABB), 26th-28th Feb 2009, Mumbai, India.

Wavelet Based Denoising for Suppression of Motion Artifacts

in Impedance Cardiography By

V.K. Pandey <[email protected]>

P.C. Pandey <[email protected]>

IIT Bombay, India

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Abstract

Impedance cardiography is a noninvasive technique for monitoring the stroke volume and some other cardiovascular indices, based on sensing the changes in the electrical impedance of the thorax, caused by variation in the blood volume during the cardiac cycle. Respiratory and motion artifacts cause baseline drift in the sensed impedance waveform, particularly during or after exercise, and this drift results in errors in the estimation of the parameters. In the present study, we examine the applicability of FIR Meyer wavelet based linear denoising technique, investigated earlier for suppression of the respiratory artifact, for cancellation of the motion artifact, without smearing the beat-to-beat variations in the parameters.

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Presentation OverviewPresentation Overview

● Introduction

● Method

● Results

● Conclusion

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●● IntroductionIntroduction● Method

● Results

● Conclusion

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Impedance Cardiography

A noninvasive technique for monitoring

stroke volume & obtaining diagnostic information on

cardiovascular functioning

by sensing variation in thoracic impedance due to change in

blood volume and based on a thoracic impedance model

Established methods for SV measurement• Electromagnetic flowmeter

• Thermodilution method• Fick’s dye dilution method• CO2 rebreathing method• Doppler Echocardiography

Introduction

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Origin of the thoracic impedance (Sensing by 4 - electrode configuration)

• Conductive path way: vena cava & thoracic aorta

• Intercostal muscle & less conducting lungs tissues• Non conducting ribs: perpendicular to the current path

Vena Cava Thoracic Aorta

VoltageSensing

CurrentInjection

Introduction (contd)

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Contributions to changes in thoracic impedance

• Cardiovascular activity

- pulsating blood flow: aorta & pulmonary artery

• Erythrocytes orientation

- acceleration of blood

• Respiration

- change in intra-thoracic pressure

• Motion

- change in thoracic dimension


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Impedance model of the thorax

• Thoracic region modeled as a conductor of fixed length and variable cross-sectional area• An increase in the volume of the blood in the region → decrease in its resistance

• Stroke volume

∆V = stroke volume (mL), ρ = resistivity of blood (Ω-cm), L = the length of the modeled

conductor (cm), Zo = the basal impedance (Ω), (- dz/dt)max = the maximum of the derivative

of the impedance during the systole (Ω/s), Tlvet = left ventricle ejection time (s)

• Cardiac output = SV х HR

2

2max

lveto

L dzV T

dtZ


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Impedance cardiograph technique

ICG : (- dz/dt) waveform

Parameters for calculating the stroke

volume : (- dz/dt)max and Tlvet

Point B : aortic valve opening (1st heart sound in PCG)

Point X : aortic valve closure (2nd heart sound in PCG)

Tlvet : time diff. between the point B & X

Adopted from Malmivuo, J., and Plonsey, R. (1995). Bioelectromagnetism (2nd ed., Oxford Univ. Press, New York).


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Instrumentation

• High freq. (20-100 kHz) and low amplitude (1-5 mA) current injected• Amplitude modulated voltage picked up• Voltage signal demodulated processed to get SV and CO

• Zo ≈ 20 , z(t) < 0.2 , (-dz/dt)max < 1.5 /s


ICG Instru.amp.

Amplitude demod.

Baseline restoration

circuitECG Instru. amp.

Current source

Zo

ECG

Differentiator dz/dt

z(t)

E1

E2I1

I2

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Problems with impedance cardiography

● Simplified assumption in formulas for stroke volume estimation

● Presence of respiratory and motion artifacts in the sensed ICG

Research objective

Investigation of a technique for suppression of the artifacts from

the thoracic impedance signal,

for estimation of the stroke volume and

other cardiovascular indices on beat-to-beat basis


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● Artifacts in the sensed thoracic impedance signal

Respiration change in thoracic air volume & thoracic dimension

Motion change in thoracic dimension

Respiratory & motion artifacts in ICG ◦ low frequency & large amplitude ◦ result in base line variation ◦ spectra partly overlap with that of ICG ◦ errors in detection of B and X points error in

calculating Tlvet


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● Solutions

♦ Holding breath ◦ Change in SV ◦ Difficult during or after exercise

♦ Ensemble averaging of ICG(Qu et al, 1968; Zhang et al, 1986; Hurwitz et al, 1988; Riese et al, 2003) R-point synchronized time frames ( R-point – 1/8 * R-R interval, R-point + 3/4 * R-R interval) ◦ Beat-to-beat relation lost◦ Smudging of ICG peaks◦ Shifting, blurring or loss of B & X points

♦ Narrow band IIR filter, centered around HR (Yamamota et al, 1988)

◦ Nonlinear phase

◦ Attenuation of high freq. component of ICG


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♦ HP IIR digital filter with cardio-respiratory synchronization (Raza et al,

1992): distortion of ICG signal during or after exercise

♦ Adaptive filter with scaled Fourier linear combiner (Barros et al, 1995): may produce a distorted output due to variation in time difference between the electrical and mechanical activities of the heart

♦ Wavelet-based method based on soft thresholding (Ouyong et al, 1998): uses soft thresholding, breath holding for 8 s is needed to construct the auto-regressive model of the cardiac signal

♦ Adaptive filter with sensed respiration as reference, simultaneously acquired with ICG (Pandey et al, 2005)

◦ Fail to suppress higher harmonics of the respiratory artifacts ◦ Not suitable for suppressing motion artifacts due to difficulty in sensing the references related to the sources of various motions

♦ Decomposition with specific orthonormal basis (Krivoshei et al, 2008): can not track fast variation, no validation/ evaluation for this technique reported by authors


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● Introduction

● MethodMethod● Results

● Conclusion

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Wavelet based denoising (WBD) technique

Removal of respiratory & motion artifacts, for preserving

beat-to-beat variation in the estimated SV and other indices, without using a reference signal

♦ Application of wavelet based denoising for suppression of respiratory artifact (Pandey & Pandey, 2007)

1. Processing of signals with simulated respiratory artifact (-9 to 9 dB): SNR improvement of 21.8 dB.

2. Application for beat-to-beat SV estimation with Doppler echocardiography as a reference technique on post-exercise recordings (9 subjects): correlation coefficients 0.35-0.80 changed to 0.87-0.98.

♦ Application of the technique for suppression of motion artifact

Method

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DWT based linear denoising (scale dependent thresholding)

♦ Decomposition of signal (S.R. = 500 Hz) using dyadic multiresolution analysis into details and the approximation, using

. Daubechies

. Coiflets

. Symlets

. FIR Meyer

♦ FIR based Meyer wavelet analysis (10 scales): ICG & z(t) captured in first 8 scales while the artifact in higher scales

♦ Basic steps:1) Calculate the DWT coefficients up to 10 scales

2) Reconstruction of signal with D1- D8.

Method (contd)

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Experimental Set-up

♦ Acquisition of ICG, z(t), Zo, & ECG

◦ ICG instrument (developed at IIT Bombay) and 4-electrode configurationInjecting a current (≈ 100 kHz, < 5 mA) through the outer electrode pair (upper part of the neck, abdomen) & sensing the resulting AM voltage across inner electrode pair (lower part of neck, level of xiphoid and sternum)

◦ USB based signal acquisition unit . Sampling rate : 500 Sa/s . Quantization : 12 bit .

Recording length : 5 min.

Recordings: Taken with breath hold to avoid respiratory artifact

Subjects7 professional swimmers(age 21-35 years, with no known cardiovascular disorders)

Method (contd)

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● Introduction

● Method

● ResultsResults● Conclusion

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Processing of ICG from subject ‘JJ’ taken with the breath held for 30 s and left hand movement(a) recorded ICG (in Ω/s), (b) estimated artifact (in arbitrary units), (c) processed ICG (in Ω/s).

Results

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Results (contd)

Processing of ICG from subject ‘SY’ taken with the breath held for 20 s and jogging at slow pace(a) recorded ICG (in Ω/s), (b) estimated artifact (in arbitrary units), (c) processed ICG (in Ω/s).

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• Distortion in the artifact-free output: -33.2 dB

• Denoised ICG

. No visible motion artifacts

. Stable ICG peaks and characteristic points

. Improved estimation of parameters from ICG

Result Summary

Results (contd)

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●● Introduction

● Method

● Results

● ConclusionConclusion

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Conclusion

● Wavelet based denoising suppresses respiratory & motion

artifacts present in ICG, with negligible distortion.

● Technique enables cycle-by-cycle stroke volume (& cardiac

output) calculation, useful during exercise or post-exercise

recordings, where cardiac activity is rapidly changing.

Conclusion

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