Analysis of the Electromechanical Activity of the Heart from

Synchronized ECG and PCG Signals of Subjects Under Stress

Ana Castro

, Ali Moukadem

, Samuel Schmidt

, Alain Dieterlen

and Miguel T. Coimbra

Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

MIPS - IUT, Universit

e de Haute-Alsace, Mulhouse, France

Department of Health Science and Technology, Aalborg University, Aalborg, Denmark

Instituto de Telecomunicac¸

oes, Departamento de Ci

encia de Computadores, Faculdade de Ci

encias,

Universidade do Porto, Porto, Portugal

Keywords:

Electrocardiogram, Phonocardiogram, Heart Sound, Exercise, Stress.

Abstract:

In this exploratory study we propose to analyze, in healthy adult volunteers, the heart electrical (electrocar-

diogram, ECG) and mechanical (phonocardiogram, PCG) activity during exercise. Heart sounds amplitude,

frequency content, and RS2, may be important features in the non-invasive assessment of heart activity, such

as for the estimation of cardiac output and blood pressure. Nine healthy volunteers were monitored with ECG

and PCG simultaneously, under a stress test. After each workload level a 10 s window of signal was collected.

PCG ﬁrst (S1) and second (S2) heart sounds were manually annotated, based on time of QRS complex occur-

rence. A QRS detector was implemented to detect the QRS complex, and time intervals between electrical

and mechanical events. Extracted features were analyzed in relation to heart rate (HR), including RS2, S1

and S2 amplitudes, and high frequency content of S1 and S2. Spearman correlation was used. Changes be-

tween baseline and maximum workload stage/HR for each volunteer were analyzed. Signiﬁcant correlation

was observed between HR, and all characteristics extracted (P<0.01). There was a clear difference between

all variables from baseline to maximum workload level: with increasing workload/HR heart sounds amplitude

increased (more pronounced in S1), RS2 decreased, and high frequency content of S2 decreased in relation

to the high frequency content of S1, demonstrating that dynamic cardiovascular relations are individualized

during cardiac stress and that assumptions for resting conditions may not be assumed.

1 INTRODUCTION

The heart is the center of the cardiovascular system,

pumping blood to the pulmonary and systemic circu-

lations, taking oxygen and nutrients to all organs.

During exercise, the muscles need for oxygen and

nutrients, leads to an increase in heart activity and res-

piration rate. It is known that stroke volume (SV) in

athletes is superior to the normal person, nonetheless

in rest condition the cardiac output (CO) is compara-

ble between the two, since heart rate (HR) tends to be

lower in the athletes.

With increasing levels of exercise, oxygen con-

sumption increases, leading to an elevated CO. The

CO increases as a function of increased HR (major

responsible for this increase), and SV, which tends to

stabilize in a plateau (Guyton and Hall, 2006). Also,

the blood pressure (BP) during exercise is increased

(Sullivan et al., 1989).

Heart function may be monitored through differ-

ent means, such as electrical activity (electrocardio-

gram, ECG), and the register of mechanical activity

through ecocardiography, or heart sound auscultation

(phonocardiogram, PCG). In the PCG, the heart cy-

cle period (S11) is depicted in four main components:

the ﬁrst sound (S1) corresponding to the closure of

mitral and tricuspid valves, the systolic period (S12),

the second sound (S2) corresponding to the closure

of the aortic and pulmonary valves, and the diastolic

period (S21) (Guyton and Hall, 2006).

The correct identiﬁcation of heart sounds allows

retrieval of information from each component, includ-

ing the detection of murmurs and their characteristics,

which is very important for cardiovascular patholo-

gies screening. The S2 is described as being com-

posed by two components: the ﬁrst related to the clo-

sure of the aortic valve (A2) and systemic BP, and a

second component related to the closure of the pul-

Castro A., Moukadem A., Schmidt S., Dieterlen A. and T. Coimbra M..

Analysis of the Electromechanical Activity of the Heart from Synchronized ECG and PCG Signals of Subjects Under Stress.

DOI: 10.5220/0005202400490056

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2015), pages 49-56

ISBN: 978-989-758-069-7

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

monary valve (P2) and pulmonary BP (Guyton and

Hall, 2006; Xu et al., 2001). Previous studies have

demonstrated a relation between S2 characteristics

such as amplitude and frequency, and the arterial BP

both on the systemic (Bartels and Harder, 1992) and

pulmonary circulations (Xu et al., 2001; Smith and

Ventura, 2013).

Electrical and mechanical time events in the heart

cycle have also been explored to better understand

cardiovascular function, using pulse transit time (Sola

et al., 2011), vascular transit time (Chandrasekaran

et al., 2013), RS2 timing (Figure 1) (Zhang et al.,

2008) and their relation to BP and CO, in the search

for non-invasive estimations of these variables.

Figure 1: Representation of the heart electromechanical ac-

tivity as measured by the electrocardiogram (ECG) and the

phonocardiogram (PCG), with time intervals of interest.

Individual response to cardiac stress is dependent

on baseline conditions and dynamic adaptations dur-

ing exercise, nonetheless assumptions for resting con-

ditions may not me assumed under cardiac stress. The

study of the dynamic relations between electrical and

mechanical cardiac events may introduce important

information for the non-invasive assessment of car-

diac state (CO and BP e.g.) (Zheng et al., 2014). In

this exploratory study it was aimed at analyzing, in

adult volunteers under different exercise levels, the

evolution of the heart electromechanical activity un-

til stress is reached, including RS2 time interval and

the amplitude and frequency characteristics of heart

sounds, in order to better understand these relations

during exercise.

2 MATERIAL AND METHODS

2.1 Data Collection

To study experimentally the variation of some physi-

ological parameters in the ECG and PCG signals un-

der a stress test, data collected from healthy subjects

from the department of Health Science and Technol-

ogy, Aalborg University is used in this paper (Ronved

et al., 2011).

Nine healthy subjects were enrolled in the study,

four females and ﬁve males, with median age 32 (24-

36). Informed consent was retrieved from all subjects

prior to the exercise test.

A Panasonic microphone was incorporated in a

coupler, specially designed by the Department of

Acoustics at Aalborg University, Denmark. The mi-

crophone detects the mechanical pressure differences

in the coupler, caused by alterations of the sound pres-

sure. The microphone records with a sampling fre-

quency of 48000 Hz. The microphone was ﬁtted to

the 3rd left intercostal space with a specially designed

double adhesive plaster. The heart sound recordings

are synchronized with a 3-lead ECG.

Following monitoring, the subject cycled on a

Monark Ergometric 894E ergometer bicycle, and the

workload was increased by 25 Watt every two min-

utes, with an initial workload of 25 watt. The sub-

ject cycled until subjective maximum endurance was

reached. Afterward subjects that did not reach 80%

of maximum heart rate deﬁned as 220 minus age±12

were excluded from the study. One subject did not

reach this rate and was therefore excluded from the

study. Recordings of heart sounds were made for 10

seconds at the end of each workload level.

An Acarix Data Acquisition System was used for

recording the heart sounds and ECG (Hansen et al.,

2011).

2.2 Electromechanical Activity Analysis

PCG and ECG signals were visually inspected, and

the heart sounds manually annotated based on the

point where the variation from the baseline began

(Ronved et al., 2011).

For the ECG, a QRS detector based on the wavelet

transform was implemented. A zero-phase, Butter-

worth band-pass ﬁlter was applied to the data (10-

25 Hz), and the wavelet transform level 8 was ob-

tained. Energy peaks of detail coefﬁcients at levels

7 and 8 were used in a peak-picking algorithm to de-

tect the QRS complex (Kohler et al., 2002; Zidelmal

et al., 2012).

Figure 2 presents the collected signals in one of

the volunteers in the study, including the manually an-

notated heart sounds, and detected QRS complexes.

Markers from the ECG were crossed with the

PCG markers to obtain the electromechanical time in-

tervals, for the different workload levels. For each

heart sound cycle manually annotated, the algorithm

searched for the corresponding QRS complex (within

BIOSIGNALS2015-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

Figure 2: Data from one of the volunteers in the study, at

rest condition: on top the electrocardiogram (ECG), and be-

low the corresponding phonocardiogram (PCG) with over-

lapping markers of the ﬁrst heart (S1) and second (S2) heart

sounds.

a 50 ms neighborhood of S1), and when a match was

obtained, the time intervals were registered with the

corresponding workload level.

RS2, RR, S12, S21 and S11 time intervals were

extracted as the time difference between each marked

event as depicted in Figure 2.

For the assessment of the heart sounds’ ampli-

tudes the temporal contour envelopes were extracted

from each S1 and S2, based on the local maximums

and minimums of the signal, and interpolated using a

cubic Hermite spline, as may be observed in Figure

3. The contour envelopes were used to estimate the

amplitude of heart sounds. The cubic Hermite spline

was used since it produces smooth contour envelopes

of the signal without overshoot.

Figure 3: Example of the temporal positive and negative

contour envelopes of a S2.

2.3 Assessing the High Frequency

Content of Heart Sounds

Besides time analysis the frequency content of S1 and

S2 with increasing workload levels/HR was analyzed.

As referred earlier, the initial component of S1 is re-

lated to the mitral and tricuspid valve closure, due

to the contraction of ventricles, thus identifying the

onset of ventricular systole and the end of mechani-

cal diastole (Felner, 1990a). The S2 is produced by

the aortic and pulmonic valves closing when left ven-

tricular pressure decreases under diastolic aortic pres-

sure. The vibrations of S2 occur at the end of ven-

tricular contraction and identify the onset of ventricu-

lar diastole and the end of mechanical systole (Felner,

1990b).

Normally, S2 exhibits higher frequency content

than S1, however the intensity and frequency of the

S1 are affected by the velocity of the forces respon-

sible to the acceleration and deceleration of the blood

masses, which on the other hand are directly related

to the HR (Felner, 1990a).

In this paper, the high frequency content of S1 and

S2 is assessed and its variation over HR is investi-

gated. The frequency content is calculated from the

Stockwell transform (S-transform) which is a time-

frequency transform (Stockwell et al., 1996).

The S-Transform originates from two advanced

signal processing tools, the Short Time Fourier Trans-

form (STFT) and the Wavelet Transform (WT). It can

be viewed as a frequency dependent STFT or a phase

corrected WT. The S-transform is becoming a valu-

able tool applied on many signals and domains as

cardiovascular (Moukadem et al., 2013), EEG sig-

nals (Assous and Boashash, 2012), geophysics (Pin-

negar and Mansinha, 2003), power system engineer-

ing (Biswal and Dash, 2013), among others.

Figure 4 presents the heart sound S-Transform for

the same volunteer, under two different workload lev-

els.

Figure 4: The S-Transform of two sounds corresponding to

the same subject and two different heart rates (a: HR=80

bpm, b: 142 bpm) showing the high frequency content of

S2 decreases when the heart rate is higher.

2.3.1 The High Frequency Content (HFC)

First, the heart sounds are segmented into S1 and

S2 by using the algorithm proposed in (Moukadem,

2013) and the high frequency content is assessed as

follows:

HFC

(t) =

+∞

−∞

(t, f )d f (1)

AnalysisoftheElectromechanicalActivityoftheHeartfromSynchronizedECGandPCGSignalsofSubjectsUnderStress

where f ∈ [50, 250]Hz.

The function HFC

gives the high frequency con-

tent of the given signal x (which is a S1 or S2 sounds

in this case). A peak detector algorithm is then ap-

plied to assess the frequency content of each seg-

mented sound and the ratio HFC

/HFC

(noted

S2/S1 for simplicity) is calculated.

Figures 5 and 6 show an example of heart sounds

selected from the database and the detected peaks on

the HFC function which aim to assess the high fre-

quency content of S1 and S2. We may observe that

with increasing HR, the HFC of S2 is decreased.

Figure 5: Heart sounds selected form the database (top), the

S-transform (middle), the HFC function and the detected

peaks where HFC

< HFC

(bottom).

Figure 6: Heart sounds selected form the database (top), the

S-transform (middle), the HFC function and the detected

peaks where HFC

> HFC

(bottom).

2.4 Statistical Analysis

Data collected from nine volunteers was down-

sampled to 2 kHz, and analyzed for each selected

workload level. One of the volunteers was excluded

from the analysis due to protocol deviation. The eight

volunteers started the experiment with different base-

line HR values for the ﬁrst workload (76±17.6), and

reached different workload levels (9, 12, 12, 7, 10, 14,

8, 6).

For each subject four heart cycles are selected at

four different stress levels (different HR), then the

mean and the standard-deviation of the extracted fea-

tures are calculated in relation to the HR. Extracted

features include HR, RS2, S1 and S2 amplitudes, and

S1/S2 HFC ratio.

Features were then analyzed in relation to the HR

(Spearman correlation coefﬁcient ρ), for the total data

and for each subject individually.

Baseline conditions versus the maximum exercise

workload achieved were compared using error bar

plots.

3 RESULTS AND DISCUSSION

3.1 Electromechanical Activity of the

Heart

It was observed that the RS2 time intervals decreased

with increasing workload in all volunteers, as demon-

strated in Figure 7. This decrease in time from elec-

trical on-set to mechanical event (aortic valve closure)

was accompanied by an increase in heart sounds’ am-

plitude. S1 amplitude had a more pronounced in-

crease than S2 as may be observed in Figure 8.

Table 1 presents the Spearman correlation coefﬁ-

cient values between the different metrics extracted

from the PCG and ECG, and the workload levels/HR.

Table 1: Correlation between workload, HR and the vari-

ables extracted from the PCG and ECG, for the total dataset,

and the average for each subject individually (* P<0.01).

Total (ρ) Average Individual (ρ)

RS2 -0.88* -0.86

S1 Amplitude 0.47* 0.63

S2 Amplitude 0.12 0.29

S2/S1 -0.70 -0.88

We may observe that RS2 presents the highest cor-

relation values for the total data set. This was also ob-

served within each subject data. RS2 has been linked

to blood pressure, which is also increased during ex-

ercise. This may conﬁgure a good indicator of car-

diovascular changes during exercise, such as CO, BP,

pre-ejection period (PEP), and the left ventricle ejec-

tion time (LVET) (Paiva et al., 2009; Carvalho et al.,

2010; Sola et al., 2011; Sola et al., 2013). RS2 val-

ues present a high correlation with HR, both using the

total data, as the individual changes, which indicates

that this variable was similar among subjects (base-

line values and evolution with HR).

BIOSIGNALS2015-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

Figure 7: Variation of the RS2 interval over the HR for all subjects.

S1 amplitude also presented a good correlation

with the workload/HR, however we should highlight

that heart sound amplitudes are highly dependent on

the acquisition conditions. Heart sounds amplitudes

are known to vary with subjects’ biotype, as well

as microphone positioning and gain, representing a

bias in the comparison of values for different subjects

(subject 3 on Figure 8 e.g.). This was conﬁrmed by

the lower correlation for the total data set, when com-

pared to the individual assessment.

In Figure 8 it is observed that S1 and S2 ampli-

tudes present an increasing tendency with increas-

ing stress levels (except subject 2), but with different

baseline values for each subject. It is also observed

that the amplitude ratio between S1 and S2 ampli-

tudes are different at baseline, which may be a result

of microphone positioning and subjects’ biotype, as

referred earlier. This however does not diminish the

information contained in these signals on the mechan-

ical activity of the heart under stress tests, if one con-

siders an individual calibration, as suggested in (Bar-

tels and Harder, 1992). Heart sounds amplitude and

frequency content have been used in previous stud-

ies to estimate systemic and pulmonary BP (Bartels

and Harder, 1992; Smith and Ventura, 2013; Dennis

et al., 2010), and results in this preliminary study in-

dicate that they also reﬂect the known cardiovascular

changes during exercise (increased CO).

The observed changes in the electromechanical

events and heart sound characteristics are the result of

the heart effort to pump more blood to reach the tis-

sues with increasing muscular effort. It is known that

the heart morphology varies with increasing exercise

(left ventricle diameter e.g.), and depending on the

type of physical activity (Pluim et al., 2000), this re-

sults in different baseline and stress adaptations from

the cardiovascular system, which may explain the

difference between volunteers regarding the baseline

values observed, and maximum workload achieved.

The different features extracted may convey im-

portant information on the assessment of the cardio-

vascular system, and allow for the better understand-

ing of these changes during a stress test.

3.2 Assessing the High Frequency

Content of Heart Sounds

To show experimentally the relation between the fre-

quency content of heart sounds and the HR, the HFC

of S1 and S2 is estimated as described earlier. Results

for the eight subjects in the experiment considering

S2/S1 are presented in Figure 9.

The results in Figure 9 show clearly the direct re-

lation of the frequency content of the heart sounds

and the heart rate. Normally, S2 has a higher fre-

quency content than S1 (S2/S1 > 1) except for sub-

ject 5. When the heart rate increases, the high fre-

quency content of S2 decreases which decrease the

ratio S2/S1, also observed in Figures 5 and 6.

The red line in Figure 9 indicates when the fre-

quency content of S1 exceeds the frequency content

of S2. The HFC ratio (S2/S1) presented a very good

correlation with the workload/HR (-0.88). However,

a lower correlation results for the total data set is ob-

AnalysisoftheElectromechanicalActivityoftheHeartfromSynchronizedECGandPCGSignalsofSubjectsUnderStress

Figure 8: Variation of the S1 (solid line) and S2 (dashed line) amplitudes over the HR for all subjects.

Figure 9: Variation of the high frequency content ratio (S2/S1) over the HR for all subjects. The red lines indicate when the

high frequency content of S2 become lower than S1.

tained (-0.7). The frequency content of S1 and S2 is

also related to the biotype which makes the HFC mea-

sure more sensitive when compared between difﬁrent

subjects.

4 CONCLUSIONS

Investigating unobtrusive monitoring is of paramount

importance due to the society demands for long-term

monitoring in several scenarios from athletes assess-

ment, to the monitoring of the elderly at home (Zheng

et al., 2014). In this study a framework for the anal-

ysis of the cardiovascular electromechanical events

during exercise is proposed.

Several features from the ECG and PCG were

studied in adult volunteers during a exercise test in-

cluding the RS2 time interval, heart sounds amplitude

and HFC. All analyzed characteristics were related to

the increase in cardiac stress. Different baseline val-

ues were observed between subjects which may be de-

termined by their ﬁtness and biotype.

RS2 was comparable between subjects, both for

the baseline and maximum stress achieved, with a

BIOSIGNALS2015-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

good correlation both individually and for the total

data set. The assessment of the RS2 may be useful for

the non-invasive assessment of PEP and LVET during

exercise. Heart sound amplitude was also observed to

be increased with stress, the result of the cardiovascu-

lar response to the extra need for oxygenated blood in

the muscles during exercise, leading to an increase in

CO, HR and BP.

S2/S1 was decreased with increasing exercise,

and although HFC of S2 tend to be higher than of S1,

in this study we observed that during a stress test this

relation may be altered, leading to the need of differ-

ent features in the discrimination of S1 and S2 than

those usually proposed.

The description of these features allows a more

comprehensive assessment of the cardiovascular sys-

tem, and the dynamic changes that occur during a

stress test, paving the way for future studies, includ-

ing the assessment of these variables with blood pres-

sure measurement and simultaneous pulse wave am-

plitude analysis (vascular transit time).

ACKNOWLEDGEMENTS

This work was partially funded by the Fundac¸

para a Ci

encia e Tecnologia (FCT, Portuguese Foun-

dation for Science and Technology) under the ref-

erence Heart Safe PTDC/EEI-PRO/2857/2012; and

Project I-CITY - ICT for Future Health/Faculdade de

Engenharia da Universidade do Porto, NORTE-07-

0124-FEDER-000068, funded by the Fundo Europeu

de Desenvolvimento Regional (FEDER) through the

Programa Operacional do Norte (ON2) and by na-

tional funds through FCT/MEC (PIDDAC); and the

french telemedicine project E-Care.

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