ACOUSTIC INDICES OF CARDIAC FUNCTIONALITY

Guy Amit

, Jonathan Lessick

2,3

School of Computer Science, Tel-AvivUniversity, Tel-Aviv, Israel

Department of Cardiology, Rambam Medical Center, Haifa, Israel

Noam Gavriely

, Nathan Intrator

Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel

Keywords: Heart sounds, time-frequency analysis, feature extraction, cardiac functionality.

Abstract: The mechanical processes of the cardiac cycle generate vibratory and acoustic signals that are received on

the chest wall. We describe signal processing and feature extraction methods utilizing these signals for

continuous non-invasive monitoring of cardiac systolic function. Vibro-acoustic heart signals were acquired

from eleven subjects during a routine pharmacological stress echocardiography test. Principal component

analysis, applied to the joint time-frequency distribution of the first heart sound (S1), revealed a pattern of

an increase in the spectral energy and the frequency bandwidth of the signal associated with the increase of

cardiac contractility during the stress test. Novel acoustic indices of S1 that compactly describe this pattern

showed good linear correlation with reference indices of systolic functionality estimated by strain-

echocardiography. The acoustic indices may therefore be used to improve monitoring and diagnosis of

cardiac systolic dysfunctions.

1 INTRODUCTION

The human heart is a mechanical system whose

primary function is to pump blood throughout the

body in order to provide adequate perfusion of

organs. This function is achieved by a complex

interplay between the cardiac muscle, the vascular

system and the blood, highly regulated by

mechanical and neural control mechanisms.

Cardiovascular diseases, such as coronary artery

disease, hypertension and cardiomyopathy, may

impair the mechanical functionality of the heart,

leading to the clinical syndrome of heart failure

(HF). As these diseases are major public health

problems worldwide, technologies for improving

early diagnosis and patient monitoring are essential.

The low-frequency vibratory and acoustic

signals, produced by the mechanical processes of the

cardiac cycle and received on the chest wall, provide

a direct and simple way for assessing the mechanical

functionality of the cardiovascular system (Tavel,

1978). However, the utilization of these signals in

the clinical setting has been mostly limited to

qualitative assessment by manual methods, as

research and development efforts in recent years

focused on modern imaging technologies such as

echocardiography and cardiac computerized

tomography. These valuable techniques require

complex equipment, as well as expert operators and

interpreters. In particular, these imaging tools can

not be used continuously or outside of the hospital

environment. Consequently, long-term non-invasive

monitoring of mechanical functionality remains

unavailable in the common medical practice.

In this work, we revisit the problem of

quantitative analysis of mechanical vibro-acoustic

heart signals using modern signal processing tools.

In an earlier study, we have shown the feasibility of

using vibro-acoustic signals to extract temporal

information about the phases of the cardiac cycle

(Amit, 2005). In the current study, we address the

potential of continuously assessing the global

systolic functionality of the left ventricle using

indices extracted from the first heart sound, S1.

According to Rushmer’s theory of the origin of heart

sounds, S1 is generated by the vibrations of the

entire cardiohemic system, as a result of blood

acceleration and deceleration following the onset of

ventricular contraction and the closure of the

Amit G., Lessick J., Gavriely N. and Intrator N. (2008).

ACOUSTIC INDICES OF CARDIAC FUNCTIONALITY.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 77-83

DOI: 10.5220/0001058700770083

 SciTePress

atrioventricular valves (Rushmer, 1978). The

amplitude of S1 has been previously shown to be

related to the pressure gradient (dP/dt) developing in

the left ventricle during isovolumetric contraction

(Sakamoto, 1965). A good correlation was also

reported between dP/dt and the instantaneous

frequency of S1 (Chen, 1997). While these previous

studies were performed on anesthetized dogs, the

relation between the characteristics of S1 and global

left-ventricular systolic functionality has not been

studied in humans in routine clinical settings.

We study the relationship between acoustic

indices, extracted from the time-frequency energy

distribution of S1, and reference echocardiographic

indices that are related to left-ventricular systolic

functionality. To achieve dynamic, yet controllable,

hemodynamic conditions, we used clinical settings

of a routine echocardiography pharmacological

stress test. In the following sections, we describe the

signal processing and feature extraction methods

applied to the vibro-acoustic heart signal, introduce

novel acoustic indices of systolic functionality and

present quantitative results on the correlation

between these indices and echocardiography-derived

measures. We conclude by discussing the potential

applicability of our methods for continuous non-

invasive monitoring of cardiac systolic function.

2 METHODS

2.1 Patients and Protocol

The study was approved by the local ethics

committee for medical research. Data was acquired

from eleven male subjects of ages 36-79 (mean

60±14), referred to a routine Dobutamine stress echo

test (DSE) for assessment of ischemic heart disease.

The referral indications included positive ergometry

stress test, atypical chest pain and chest pain during

physical activity. Two of the subjects had a history

of coronary artery disease. These two subjects were

diagnosed as positive for myocardial ischemia in the

DSE test. The remaining nine patients were

diagnosed as negative for ischemic heart disease.

Prior to data recording, the patients signed an

informed consent form. The standard DSE protocol

consisted of four 3-minute stages of increasing

Dobutamine dosage, from 10 to 40µg/kg/min. If the

target heart rate, defined as 0.85 * (220 – Age), was

not achieved at the end of the final stage, 0·25 mg

boluses of atropine were given at 1-min intervals, up

to a maximum of 1 mg.

2.2 Data Acquisition

Vibro-acoustic heart signals were recorded using a

digital data acquisition system constructed in our

lab. The system consisted of 4 piezoelectric contact

transducers (PPG Sensor Model 3, OHK Medical

Devices, Haifa, Israel), an ECG sensor (EKG-BTA,

Vernier Software & Technology, Beaverton, OR), a

preamplifier with high input impedance and a linear

frequency range of 1Hz – 4KHz (A.S. ZLIL, Bnei-

Brak, Israel), a 16-bit analog-to-digital converter

(PMD-1608FS, Measurement Computing Corp.,

Norton, MA), and a designated signal recording

software running on a portable personal computer.

The transducers were placed at the apex area, the

aortic and pulmonary areas (second

intercostal

space, right and left sternal border, respectively) and

at the right carotid artery, and were firmly attached

using either elastic straps or adhesive bands. The

patients were lying on their left side. Vibro-acoustic

and ECG signals were continuously recorded during

the stress test (30-45 minutes long) at a sample rate

of 4KHz. Echocardiography images were acquired

using a GE Vivid 7 ultrasound machine (General

Electric Healthcare, Wauwatosa, WI). Two-

dimensional echo cine loops of a single heart beat

were captured before the beginning of the stress test

(baseline), during each stage of the test and

following the test (recovery), from three apical

views (4-chamber, 2-chamber and apical long axis)

at a high frame rate of 70-100 FPS.

2.3 Echo Data Processing

The captured echo cine loops were post-processed

using EchoPAC Dimension ’06 software (GE

Healthcare Wauwatosa, WI) in order to extract

quantitative echocardiographic indices of systolic

functionality. The indices used were peak systolic

velocity (PSV) and peak systolic strain rate (PSSR),

shown to be strongly correlated with left-ventricular

systolic functionality (Greenberg, 2002). These

indices were first calculated separately for each

cardiac wall (septal, lateral, inferior, anterior,

posterior, and anteroseptal) and for three segments

per wall (basal, middle and apical), and then

averaged to obtain a global functionality index.

Index calculation was done using 2D strain analysis,

based on speckle tracking technique. This modality

allows objective analysis of the entire myocardial

motion throughout the heart cycle by tracking

natural acoustic markers in the image. It was shown

to provide accurate strain measurements, compared

with tagged MRI (Amundsen, 2006). Strain indices

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

were successfully calculated for 10 patients. One

patient was excluded due to inadequate quality of

the captured echo images.

2.4 Acoustic Signal Processing

Each of the four recorded signal channels was first

pre-processed by a applying a digital band-pass filter

in the frequency range of 20-250Hz (Figure 1a). The

signal was then partitioned into cardiac cycles using

the peaks of the ECG-QRS complexes as reference

points (Figure 1b). Signal segments with noisy ECG

were excluded from the analysis. The signal cycles

were aligned by their starting points and their

amplitudes were color-coded to create a two-

dimensional signal map, showing the time-domain

dynamics of the first and second heart sounds

throughout the stress test (Figure 1c). Fast Fourier

transform (FFT) was applied to each cycle of the

first heart sound (S1), defined as the cycle segment

from 50ms before the QRS peak to 200ms after the

QRS peak. The logarithm of the power spectrums

was color-coded to generate a spectral map of S1

throughout the recording (Figure 1d).

(a)

(b) (c) (d)

Figure 1: Generation of time-domain and frequency-

domain signal maps in a healthy subject: (a) the heart

sound signal (blue), segmented using the ECG (red). (b)

aligned multiple sound signal cycles throughout the test

(left y-axis), with heart rate (red labels) and test staged

(white labels and colored segments), (c) continuous color-

coded map of segmented sound signals (d) continuous

color-coded power spectrum of the first heart sound (S1).

In order to characterize the joint time-frequency

energy distribution of S1, S-transform was applied

to each cycle of S1. S-transform (Stockwell, 1996) is

a linear transform that provides frequency-dependent

resolution, while maintaining a direct relationship

with the Fourier spectrum. It is defined by:

(-)

-2

(, ) ()

ift

Sf st e e dt

ℜ

∫

Where s(t) is the original signal, τ is the time delay

and f is the frequency. The progressive resolution of

the transform provides a time-frequency resolution

superior to Fourier-based techniques, while its

linearity ensures accurate decomposition without

artifactual cross-terms that are typical to quadratic

transforms. S-transform is therefore suitable for

analysis of non-stationary multi-component signals

such as heart sounds.

After applying S-transform to each cycle of S1, the

resulting time-frequency representations were

grouped by the stages of the stress test and averaged

to produce a small number of representative time-

frequency maps (

Figure 2).

Figure 2: S-transform time-frequency representation of S1

acoustic signal obtained in a representative healthy subject

during the stages of the stress test. Each plot represents an

average of the S-transform of all S1 cycles over a

specified period of the test.

2.5 Acoustic Feature Extraction

The purpose of feature extraction is to find a

compact representation of high dimensional data,

without significant loss of information content.

Principal component analysis (PCA) is a well-

known statistical technique for dimensionality

reduction (Duda, 1973). The principle of PCA is to

project the data on a new orthogonal basis, such that

the variances of the linearly transformed data are

sorted in descending order along the coordinates,

with the maximal variance on the first coordinate

ACOUSTIC INDICES OF CARDIAC FUNCTIONALITY

(first principle component), the second largest

variance on the second coordinate, and so on. The

projection of the original data on the first few

principal components provides a low-dimensional

representation of the data, which emphasizes the

significant features (in terms of statistical

variability) in the data. The choice of the significant

principal components is done by examining their

associated eigenvalues.

PCA was applied on the aggregation of

segmented S1 signals. The analysis was performed

on both the frequency domain spectral maps (Figure

1d) and on the time-frequency representations

produced by the S-transform (Figure 2), vectorized

by concatenating adjacent columns. The most

significant principal components, having

eigenvalues greater than 10% of the first eigenvalue,

were selected and weighted by their relative

eigenvalues. The projection of the data on this

weighted combination of the significant principal

components was chosen as a one-dimensional

feature representing the dynamic characteristics of

the acoustic signal during the stress test. To obtain

an interpretable trend line, this feature was

normalized by the median value of the baseline stage

and smoothed by a moving average filter. The

resulting index, denoted acoustic variability index

(AVI) is interpreted as the trend of relative change in

the spectral energy distribution of S1.

Figure 3: Principal component analysis applied to the

vectroized time-frequency representation of S1 cycles

during a stress test. The bottom plots show the coefficients

of the first 3 principal components (PC), and their linear

combination, weighted by the eigenvalues (shown in

parenthesis). The upper plots show the AVI index during

the entire stress test, obtained by projecting the data on the

respective PC. The red lines are the result of smoothing

the projected data with a moving-average filter.

Figure

3 illustrates an example of applying PCA to

the time-frequency data shown in

Figure 2, and

calculating the time-frequency AVI.

A second feature extracted from the spectrum of

each cycle of S1 was the frequency bandwidth of the

signal, defined by the highest frequency with

significant energy content. Prior to calculating this

feature, signal cycles with a high wide-band energy

content, compared to their local environment, were

classified as noise and excluded from further

processing. The bandwidth feature was calculated

for each cycle by searching the spectrum for the first

frequency whose energy is at least 10dB below the

maximal energy. The feature trend line obtained

from all cycles was normalized by the median value

of the baseline stage, and denoted Acosutic Spectral

Index (ASI).

3 RESULTS

The color-coded signal map in figure 1c illustrates

the time-domain characteristics of the heart sound

signal during the stress test. As expected, there are

noticeable changes in the duration of ventricular

systole and diastole, as the heart rate increases in

exercise and decreases in recovery. However, there

are no apparent morphological changes in the signal

that can be associated with the stress response.

Fourier analysis uncovers a pattern of an ascent in

the spectral energy of the first heart sound as the

Dobutamine dose is increased, and a descent back to

baseline levels during recovery figure 1d. In addition

to the overall energy rise, there is also an increase in

the frequency bandwidth of S1, as higher frequency

components in the range of 50-150Hz emerge and

strengthen. The time-frequency representation,

obtained by S-transform, enables localization of

these spectral changes in time (Figure 2): the high-

frequency components are centered about 80ms after

the beginning of the signal (30ms after the peak of

ECG-QRS complex), growing up to 150Hz in the

highest Dobutamine dose, then falling back to the

baseline upper-limit frequency of 50Hz in the

recovery phase. There is no apparent time shift of

the signal’s energy distribution throughout the test.

Principal component analysis, applied to the

spectral maps of S1, was able to identify the major

frequency bands that contribute to the data

variability. When applied to the vectorized time-

frequency distributions, PCA also pointed out the

temporal location of these frequency bands. Figure 3

shows a representative example of the coefficients of

the first three principal components (PC), and the

projection of the time-frequency data on these

principal components. The first PC, representing the

axis with the largest data variability, captures the

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

pattern already observed qualitatively in the time-

frequency distributions in

Figure 2: it varies from

30ms to 120ms relative to the beginning of the

cycle, and from frequency of 20Hz to 70Hz, thus

showing the strengthening of the signal’s low-

frequency components. The second PC captures the

variability of the high frequency components

between 110 to 150Hz for the entire duration of the

S1 signal. The third PC shows a wide-band

variability of frequency ranging from 40Hz to

150Hz, localized in time around 80ms from the

beginning of the cycle. This component strengthens

during the peak stress response. A combination of

the most significant principal components, weighted

by their eigenvalues, and the projection of the data

on this combined PC provide a one-dimensional

feature, denoted time-frequency acoustic variability

index (TF-AVI), which summarizes the dynamics of

the joint time-frequency energy distribution of S1

throughout the stress test.

The TF-AVI trend lines, extracted separately

from each of the four transducers in two

representative subjects are plotted in Figure 4, along

with the stages of the stress test, the heart rate and

blood pressure trends and the relative change in the

echocardiographic indices of peak systolic velocity

(PSV) and peak systolic strain rate (PSSR). While

the TF-AVI provides a continuous line with one

point per cardiac cycle, the reference

echocardiographic indices are available only at

discrete time points of each stage in the stress test.

Nevertheless, there is a noticeable correlation

between the two indices: for the plot in Figure 4a

(subject #5), the correlation coefficients between the

echo indices PSV and PSSR and the corresponding

TF-AVI, averaged over all transducers were 0.91

and 0.89 respectively. For the plot in Figure 4b

(subject #6) the correlation coefficients were 0.97

and 0.83 (p < 0.05 in all cases).

Both paired and unpaired t-test showed that the

absolute values of the acoustic spectral index (ASI)

at the end of low-dose Dobutamine induction were

significantly higher than the baseline values (p<0.04

for the 10ug stage, p<0.003 for the 20ug stage,

Figure 5). The correspondence between the ASI and

the echocardiographic indices in all of the subjects

was tested by comparing the values of the relative

index change at the end of the low-dose Dobutamine

stages. These points were selected since the

inotropic effect is more prominent at the early stages

of the test. In addition, the higher heart rates at later

stages of the test reduce the reliability of the tissue

tracking procedure used to extract the reference

echocardiographic indices. As shown in

Figure 6, a

good linear correlation (r=0.78, p<0.01) was

observed between ASI calculated from the apex

signal and the relative PSSR at the end of the 20ug

stage. At the end of the 10ug stage the correlation

coefficient between the two indices was 0.68

(p< 0.03).

(

)

(

)

Figure 4: TF-AVI indices of subjects #5 (a) and #6 (b).

Each plot displays the trend lines of TF-AVI from the

transducers at the apex (acg), aortic area (hsr) pulmonary

area (hsl) and carotid artery (cp), along with the relative

echo indices PSV and PSSR, trend lines of heart rate and

blood pressure, and color-coded stages of the stress test.

See text for details.

Figure 5: Absolute ASI values of all subjects at baseline

and after low-dose Dobutamine induction (10 and 20

ug/kg/min). The box plot displays the median, lower

quartile, upper quartile and data extent. Each marker

symbol represents a different subject. The p-values

represent a t-test comparison to the baseline values.

ACOUSTIC INDICES OF CARDIAC FUNCTIONALITY

Figure 6: The correlation and regression line between

relative PSSR index and relative ASI at the end of first

(10ug) and second (20ug) low-dose Dobutamine

induction. Each marker symbol represents a different

subject.

4 DISCUSSION

More than 40 years ago, Sakamoto et al. reported a

nearly linear relationship between the amplitude of

the first heart sound, S1, and the maximum of the

time derivative of the left ventricular systolic

pressure (dP/dt) in dogs (Sakamoto, 1965). Later it

was shown that myocardial infarction in humans

caused a shift of the maximum energy of S1 to a

lower frequency range (Adolph, 1970), and that a

reduction in the spectral energy of S1 correlated well

with the presence of significant coronary artery

disease (Clarke, 1978). More recently, Chen et al.

showed a good cross-correlation between the

instantaneous frequency of S1 and dP/dt of dogs in

various contractile states (Chen, 1997). They

suggested that the resonant frequency of S1 is

proportional to the fractional power of the tension of

the left-ventricular myocardium during contraction,

which relates to the left ventricular pressure gradient

by Laplaces’s law. The results of the current study

are in agreement with these previous studies

regarding the relation between the amplitude and

frequency spectrum of the first heart sound and the

dynamics of left ventricular contraction. The

acoustic indices developed in the current study

exhibited a marked correlation with the pattern of

inotropic and chronotropic changes throughout the

Dobutamine stress test. The increase in the spectral

energy, along with the emergence of higher

frequency components was consistently observed in

multiple recording locations in all of the subjects.

Although the study was conducted on a small group

of subjects, statistically significant differences were

observed across-subjects between baseline and low-

dose Dobutamine stages, confirming the reliability

of the results. The good correlation obtained with the

reference strain echocardiography indices suggests

that the acoustic indices truly characterize the

variations in the myocardial systolic functionality.

The relationship between the cardiovascular

physiological processes and their acoustic

manifestation on the chest wall is complex and most

probably non-linear. This relationship is affected by

neurohormonal modulation of the heart’s inotropic

and chronotropic states, as well as by changes in the

properties of the thoracic cavity conducting the

acoustic vibrations. Nevertheless, this work provides

a framework and a set of computational tools for

robust quantitative analysis of vibro-acoustic heart

signals that can be utilized for non-invasive,

continuous monitoring of cardiac functionality.

The capability of this framework to diagnose a

pathologic functionality reduction could not be

addressed quantitatively in this work, due to the

small number of subjects and the fact that the great

majority of the subjects had normal cardiac

functionality. Interestingly, the single subject that

was diagnosed in the echocardiography examination

with a reduced segmental wall motion during stress,

due to myocardial ischemia (subject #10) had the

lowest values of absolute and relative ASI, as well

as the lowest values of PSSR, indicating that the

compromised wall motion might result in a

frequency reduction of the first heart sound.

The usage of strain-echocardiography indices for

evaluation of left-ventricular function is still not a

part of the common clinical practice. Nevertheless,

there are strong research evidences for the relation

between peak strain rate and the invasive

contractility measure of peak elastance (Greenberg,

2002), and for the ability of global strain indices to

detect left-ventricular systolic dysfunction. (Reisner,

2004). Strain echocardiography was therefore used

in this research as a quantitative ‘gold-standard’

reference, which can be obtained non-invasively

during the routine protocol of the stress test.

One of the major challenges of extracting

meaningful physiological information from signals

acquired in routine clinical settings is noise

robustness. The data used in this work was

contaminated with various types of noise, including

body movements, interferences of the ultrasonic

transducer and audible sounds. The signal analysis

methods used in this work were specifically

designed to cope with these types of noise. In

particular, the statistical approach of transforming

the data to a new orthogonal basis of the principal

components was able to accentuate physiologically

meaningful patterns, while diminishing noisy-related

components.

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

5 CONCLUSIONS

We have described a signal analysis framework for

robust extraction of systolic functionality indices

from acoustic heart signals. The developed tools

were constructed and tested on data from a

pharmacological stress test, with strain

echocardiography as the gold standard reference.

Using principal component analysis on the time-

frequency representation of the first heart sound we

have characterized the pattern of spectral changes

occurring during the stress test, and associated this

pattern to the alternations in systolic functionality by

showing it is linearly correlated to echocardiography

derived indices of cardiac contractility. Our analysis

framework and proposed indices can be applied to

real-time continuous monitoring of cardiac

functionality, thus enabling improved diagnosis and

management of cardiac dysfunction.

ACKNOWLEDGEMENTS

We would like to express our gratitude to the team

of the Echocardiography lab at the Rambam medical

center, for their kind assistance in data collection.

We would like to thank Dr. Zvi Friedman and

Dr. Peter Lysyansky from GE Healthcare for

providing the strain echocardiography analysis tools.

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ACOUSTIC INDICES OF CARDIAC FUNCTIONALITY