NOVEL APPROACH TO CHEST IMPEDANCE SIGNAL

ANALYSIS

Algimantas Krisciukaitis, Andrius Macas, Renata Simoliuniene

Robertas Petrolis and Zita Bertasiene

Lithuanian University of Health Sciences, Eiveniu str. 4, Kaunas, Lithuania

Keywords: Chest impedance signal, Principal component analysis, Independent component analysis.

Abstract: New wave of development of more informative and reliable diagnostic methods substituting classical

Impedance Cardiography introduced by Sramek in the 1960's was inspired by rapid development of IT

based devices in medicine. We illustrate approaches of multivariate analysis of chest impedance signals in

aim to reveal parameters reflecting detail pattern of functions of cardiovascular system.

1 INTRODUCTION

The electrical resistivity of human body organs

varies about 100-fold from about 1.6 Ωm in blood to

about 170 Ωm in bone. Within the soft tissues the

variability is about 10-fold, with about 20 Ωm in the

lung and in fat (Malmivuo 1995). Physiological

processes in chest result in the permanent changes in

chest impedance. Activity of the heart and

respiratory movements play major roles.

The amount of blood in the thorax changes as a

function of the heart cycle. During systole, the right

ventricle ejects an amount of blood into the lungs

which equals the stroke volume. At the same time

blood flows from the lungs to the left atrium. The

effect of these changes in the distribution of blood in

the thorax as a function of the heart cycle can be

determined by measuring the impedance changes of

the thorax. The amount of air in the thorax is

changing as function of the respiratory cycle. It also

results in the impedance changes of the thorax.

Permeability of lung alveoli to the blood flow is

affected by air pressure in the lungs, i.e. it is

changing as a function of respiratory cycle. Taking

into account all mentioned facts we can state that

chest impedance changes reflect several interacting

processes and quantitative evaluation of the features

of it could be of great diagnostic importance.

Impedance cardiography has been introduced by

Sramek in the 1960's as a simple and non-invasive

measurement of cardiac output which is used till

nowadays. Very simple decomposition of the chest

impedance signal (ICG) or Z by determining of

first derivative (dZ/dt) of it extracts only the

component reflecting blood volume changes (BVC)

in the thorax caused by heart activity. Cardiac output

is proportional to the amplitude of dZ/dt. However,

measured data in some cases remain controversial.

This is highly expressed in the states causing low

cardiac output syndrome cardiogenic shock, severe

arrhythmias as well as in healthy obese patients.

Rapid development of devices of digital registering

of biomedical signals and availiability of

comparatively cheap computational resources for

their proccessing have inspired new wave of

development of methods for processing of such

signals. The aim of it is to reveal more informative

features of the signal and to elaborate more reliable

diagnostic methods. Extraction of other parameters

then ejected blood volume is reported in (Ernst

1999). Respiratory movements representing

component of the chest impedance signal was

reconstructed by integration of first derivative dZ/dt

of the ICG registered by means of standard

equipment. Acording to the biophysical models

(Malmivuo 1995) chest impedance signal carries

much more diagnostic information than it is used

today. Our previous studies have shown that

structural analysis of simultaneously recorded ICG

and ECG is able to separate two major components

of chest impedance signal – BVC and respiratory

movements reflecting component. Quantitative

estimates of the shape of cardiocycles of the

extracted BVC also correlate with blood volume

ejected by heart (Tamosiunas 2006). Moreover

component reflecting BVC reflects a result of left

526

Krisciukaitis A., Macas A., Simoliuniene R., Petrolis R. and Bertasiene Z..

NOVEL APPROACH TO CHEST IMPEDANCE SIGNAL ANALYSIS.

DOI: 10.5220/0003275705260529

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 526-529

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

and right ventricle outputs, so it is also a complex

signal. Decomposition of which could realize a

possibility to evaluate separately the efficiency of

functions of both ventricles. It could have a great

value for monitoring of cardiac output in acute phase

of myocardial infarction. Such decomposition could

be realized if additional blood flow reflecting signal

could be registered somewhere apart from the chest.

Then multivariate analysis methods applied for

simultaneously recorded signals from the chest and

e.g. limbs together with ECG leads could reveal

parameters reflecting detail pattern of functions of

cardiovascular system. The aim of this paper is to

present several illustrations of application of

advanced signal processing methods used to extract

parameters representing detail status of central

hemodynamics.

2 METHODS

2.1 Signal Registration

Clinical recordings of the signals for investigation

we performed during 24h follow up of the patients

hospitalized in the acute phase of myocardial

infarction in Cardiology Clinics of Kaunas

University of Medicine (Permission of Kaunas

Region Ethics Committee for Biomedical Research

Nr. 169/2004). Chest impedance signal together with

one lead ECG was recorded by means of Heartlab™

system (Dregunas 1999) (certificate No. LS.

08.02.1957) using 12 bit resolution A/D conversion

at 1000 Hz sampling rate. 250 recordings from

patients in various states of severity of myocardial

infarction were used in the study. Ten recordings we

made from healthy volunteers in addition

simultaneously recording spirogram by means of

spirometer “VMax-229” (Sensomedics USA).

Another 10 recordings we made also from healthy

volunteers in addition simultaneously registering

limb pulse wave.

2.2 Signal Processing

2.2.1 Chest Impedance Signal

Decomposition

Method for the ICG signal decomposition is based

on combined structural analysis of synchronically

registered ECG and ICG signals. The principle of

the method is illustrated on fig.1. Automatic

detection of fiducial point of every cardiocycle –

peak of ECG R-wave (marked with crosses on the

upper trace) is made in two steps. Preliminary

detection finds time points where filtered derivative

of the ECG signal exceeds certain threshold. Final

detection is made maximizing cross-correlation of

the sliding R-wave template with current ECG signal

in the region of preliminary detected point. R-wave

template is constructed from first 10 cardiocycles of

the recording and updated after every processed

cardiocycle. Fiducial time points are always pointing

to the same phase of the ICG signal component

reflecting blood flow (solid line of trace A on fig.1).

So respiratory movements caused component of the

signal is restored by means of cubic spline

interpolation between the samples of the ICG signal

at these time points (dotted line of trace A on fig.1).

Subtraction of this component (trace B on fig.1)

from the ICG signal gives the component reflecting

only blood volume changes in the chest vessels

(trace C on fig.1).

Figure 1: ICG signal decomposition: (A) cubic spline

interpolation between samples of ICG signal

corresponding to peaks of ECG R-wave, the fiducial

points of cardiocycles; (B) - reconstructed respiratory

movements reflecting component; (C) – extracted blood

volume changes reflecting component of the ICG signal.

The 180 samples of ICG signal starting from

fiducial point is considered as samples of one

cardiocycle and is used to construct a matrix

representing all cardiocycles of one recording.

2.2.2 Quantitative Evaluation of the Shape

of the Chest Impedance Signal

Samples of the extracted cardiocycles give

redundant but comprehensive representation of the

signal shape. We used Principle Component

Analysis (PCA) (Jollife 2002) to reduce the

dimensionality of the representations. Samples of

ICG cardiocycles formed two-dimensional array:

1,1 1,2 1,

2,1 2,1 2,

,1 ,2

...

... ... ...

...

X

(1)

NOVEL APPROACH TO CHEST IMPEDANCE SIGNAL ANALYSIS

527

where x

i,j

is the i

sample of the j

cardiocycle. The

PCA transforms the original data set into a new set

of vectors (the principal components) which are

uncorrelated and each of them involves information

represented by several interrelated variables in the

original set. Every vector x

representing ordinary

ICG cardiocycle or ECG T-wave is then represented

by the linear combination of the principal

components



multiplied by coefficients w

i,k

iikk







x .

(2)

The calculated principal components are ordered so

that the very first of them retain most of the

variation present in all original variables. Thus it is

possible to perform a truncated expansion of ICG

cardiocycles by using only the first several principal

components. We expected to get one or mostly

several principal components reflecting desirable

changes. We calculated the basis functions (principal

components) as eigenvectors of the covariation

matrix R







RXX .

(3)

Calculation of the covariation matrix was performed

using MatLab

function “COV” which gave

mathematical expectation E after removing the mean

from each column. Variation or trend of coefficients

i,k

represents changes of the shape of evaluated

ICG cardiocycles. It is expected that the dynamics of

cardiac output will be reflected by the shape changes

of ICG cardiocycles and represented by changes of

one or several coefficients.

2.2.3 Extraction of Components

of Chest Impedance Signal by Means

of Independent Component Analysis

Independent component analysis is able to separate

independent source signals from the mixtures which

are linear combination of them. The minimal amount

of the mixtures given for ICA should be equal to the

number of independent source signals we want to

extract. Detailed description of the method of

Independent Component Analysis is given in

(Hyvärinen, 2001). BVC reflecting component of

chest impedance signal consists of two components

which reflect: a) pulmonary (lesser) blood

circulation; b) systemic (greater) circulation. It was

used as first mixture. Pulse wave signal

simultaneously registered from the limb, which

mainly reflects systemic (greater) circulation was

used as second mixture. ICA we used to extract two

independent components and afterwards we used

averaged cardiocycle excerpts of them as basis

functions for decomposition of every single

cardiocycle of BVC reflecting component of the

chest impedance signal.

BVC reflecting component of signal x is

represented as following:













































2221212

2121111

2221

1211

sasax

(4)

Where x

and x

are two registered signals: chest

impedance and pulse wave from the limb. Then

estimated independent components will be:

, (5)

, (6)

where W=A

-1

3 RESULTS

3.1 Adequacy of Extracted Signal

Components

Result of the adequacy test of extracted respiratory

movement representing signal component is shown

on fig.2. The extracted signal is visually identical to

the signal registered by means of spirometer during

normal breathing. Limited frequency characteristics

of the ICG registering device caused inadequacy of

the respiratory component during forced and

sustained breathing. However such cases were

comparatively rear in analyzed recordings.

0102030405060Time

Volume, l

0102030405060Time

s0102030405060Time

Volume, l

Figure 2: Illustration of the adequacy test of respiratory

movement representing signal (dashed line). Control

signal (solid line) is registered by means of spirograph.

Forced breathing starts at 21 second of the test.

3.2 Chest Impedance Signal Shape

0 200 400 600

-4

-3.8

-3.6

-3.4

-3.2

Time, ms

Rel.units

0 200 400 600

-4

-3.8

-3.6

-3.4

-3.2

Time, ms

Rel.units

0 200 400 600

Figure 3: Variety of the shapes of the ICG component

reflecting blood flow during various phases of respiratory

movement. Full inhale – solid line, full exhale – dotted

line and medium position – dashed line.

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

528

Variety of the shapes of the ICG component

reflecting blood flow during various phases of the

respiratory movement is presented in fig.3.

The quantitative estimates of shape of the

decomposed ICG signal component obtained by

means of PCA showed significant correlation with

reference cardiac output estimates obtained by

means of intermittent thermodilution. The most

important fact was that correlation coefficients of the

first and the second principal component showed

significant correlation (r= 0.6 p<0.001 and r= 0,75

p<0.001 respectfully) in cases when standard

method of the evaluation of the cardiac output by

means of first derivative of ICG failed.

3.3 Independent Component Analysis

of Chest Impedance Signal

Independent components calculated from the

cardiocycles of synchronically recorded chest

impedance signal and pulse wave from the limb are

presented on fig. 4. The linear combination of them

was used for representation of cardiocycles of

recorded chest impedance signal.

0 100 200 300 Time, ms

Figure 4: Independent components calculated from

cardiocycles of synchronically recorded chest impedance

signal and pulse wave from limb.

Coefficients of these basis functions reflect

shape changes of the signal during the whole

recording. We expected that changes of only one of

them will be correlating with respiratory

movements. If so, we can expect that especially this

component will be reflecting pulmonary (lesser)

circulation (changes in permeability of the lung

alveoli will affect it). Results on fig.5 illustrate that.

0 20 40 60 80 100 120

0 1 2 3 4 5

x 10

RESP

0 20 40 60 80 100 120

0 1 2 3 4 5

x 10

RESP

Figure 5: Coefficients of independent components (lower

traces) together with reference respiratory movements

representing signal.

4 DISCUSSION

AND CONCLUSION

Results presented in this article illustrate only the

preliminary investigations which already gave

promising results. A lot of investigations is needed

till estimates obtained by means of ICA or PCA will

give the conventional estimates of cardiac output for

clinicians. However even from these results we can

expect that at least dynamics of the estimates will be

shown what sometimes is of great diagnostic value.

We hope that novel approach to chest impedance

signal analysis started by (Ernst 1999) will be

continued including applications of advanced signal

processing methods. Hopefully the result will be less

invasive and more reliable methods for the

evaluation of detail pattern of functions of

cardiovascular system.

ACKNOWLEDGEMENTS

The work is supported by Research Council of

Lithuania (Grant: MIP-68/2010).

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