A Multi-spot Murmur Sound Detection Algorithm and Its Application to

a Pediatric and Neonate Population

Marisa Oliveira

, Jorge Oliveira

2,3

, Rui Camacho

1,4

and Carlos Ferreira

Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

Instituto Superior de Engenharia do Porto, Rua Dr. Ant

onio Bernardino de Almeida N

431, 4249-015 Porto, Portugal

Knowledge Engineering and Decision Support Research Group (GECAD),

Rua Dr. Ant

onio Bernardino de Almeida N

431, 4249-015 Porto, Portugal

LIADD-INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci

encia,

Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

Keywords:

Heart Sounds, Data Processing, Heart Auscultation, Cardiovascular Data, Machine Learning, Data Mining.

Abstract:

Cardiovascular diseases are one of the leading causes of death in the world. In low income countries, heart

auscultation is of capital importance since it is an efﬁcient and low cost method to monitor the heart. In this

paper, we propose a multi-spot system that aims to detect cardiac anomalies and to support a diagnosis in

remote areas with limited heath care response. Our proposed solutions exploits data collected from the four

main auscultation spots: Mitral, Pulmonary, Tricuspid and Aorta in a asynchronous way. From the several

multi-spot systems implemented, the best results were obtained using a bi-modal system that only processes

the Mitral and the Pulmonary spot simultaneously. Using these two spots we have achieved an accuracy

between 85.7% (smallest value, using ANN) and the best value of 91.4% (obtained with a logistic regression

algorithm). Taking into a account the pediatric population and the incident cardiac pathologies, it happens

to be the spots where the observed murmurs were most audible. We have also ﬁnd out that when using four

auscultation spots, the choice of the algorithm is of secondary priority, which does not seem to be the case

for a single auscultation spot system. With one single auscultation we have an average of 4% of difference

between the results obtained with the algorithms and with four auscultation spots we have a smaller average

of 2.1%.

1 INTRODUCTION

Cardiovascular diseases are the leading cause of death

in developed countries. It is estimated that, in 2015,

17.7 million people died from a cardiovascular dis-

ease, which represents about 31% of deaths world-

wide (OPAS/OMS, 2017). These are of particular

relevance in newborns and children and adolescents,

namely children who are born with congenital malfor-

mations, taking into account that heart disease is the

type of congenital disease responsible for more deaths

in the ﬁrst year of life than any other condition, when

epidemic etiologies are excluded (Lopes et al., 2018).

In Brazil, this problem is even more accentuated due

to socioeconomic problems. According to the Multi-

dimensional Poverty Index (IPM), in 2015, 3.8 % of

the Brazilian population, which is equivalent to about

7.8 million people, lived in a situation of poverty,

that is, lack of infrastructures, few ﬁnancial resources

for an efﬁcient screening of cardiovascular diseases,

lack of medical health care, deprivations in access to

health, access to education, access to drinking water,

sanitation and electricity (PNUD, 2019). According

to the investigation, infant mortality has a major in-

ﬂuence on the mortality rate in Brazil (PNUD, 2019).

The analysis of the heart sound might mitigate the

problem because auscultation gives a basic idea about

the state of the heart, allowing to know if patients need

close medical attention which helps to prevent deaths

by cardiovascular disease. Besides, a stethoscope has

a compact and lightweight design which makes it easy

to transport to environments with difﬁcult access. In

this paper we propose a multi-spot system that aims to

detect cardiac anomalies analysing the heart sounds in

pediatric patients.

1.1 Related Work

Salleh et al. in (Sh-Hussain et al., 2013) were fo-

cused in ﬁnding the optimal auscultation spot. They

228

Oliveira, M., Oliveira, J., Camacho, R. and Ferreira, C.

A Multi-spot Murmur Sound Detection Algorithm and Its Application to a Pediatric and Neonate Population.

DOI: 10.5220/0010262502280234

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS, pages 228-234

ISBN: 978-989-758-490-9

developed a framework based on the combination of

Time Frequency Distributions (TFD), Mel-Frequency

Cepstrum Coefﬁcient (MFCC) and Hidden Markov

Model (HMM) and evaluate the performance in vari-

ous stages to observe the relative contribution of each

stage of auscultation.

Sh-Hussain et al. in (Sh-Hussain et al., 2012), de-

veloped comparative experiments using MFCC fea-

tures, various numbers of HMM states and various

numbers of gaussian mixtures to observe the impact

of these factors on the classiﬁcation performance at

the four spots of auscultation. They evaluated in dif-

ferent stages to examine the relative contribution of

each stage of auscultation in identifying the presence

of murmurs. Pedrosa et al. in (Pedrosa et al., 2014)

developed two novel algorithms: one focuses on the

segmentation of the heart sounds into heart cycles,

based on the autocorrelation function to ﬁnd the pe-

riodic components of the PCG signal, and another

is to detect heart murmurs, based on features col-

lected from different domains and its evaluation is

performed in two ways: a arbitrary distribution be-

tween train and test set and a division according to

patients.

Kobt et al. in (Kot, 2019) developed two auto-

matic computer cardiac auscultation (ACCA) models:

a model A ACCA recognition system (machine learn-

ing (interpreter independent)) and model B ACCA

recognition system (machine learning and interpreter

dependent visual analysis). The models used machine

learning based on mel-frequency cepstral coefﬁcients

as a feature and Hidden Markov Model (HMM) as a

classiﬁer and they performed visual analysis based on

phonocardiography (PCG) and spectrogram image.

Eslamizadeh et al. in (Eslamizadeh and Barati,

2017) heart cycles were divided from heart sounds us-

ing wavelet transformer. In this paper Eslamizadeh

et al. proposed the use of an Multi-layer Perceptron

(MLP) Feed-forward ANN trained with back prop-

agation learning and modiﬁed Neighbor Annealing

(NA) algorithms, to classify segmented heart sounds

into normal and murmur classes.

Kang et al. in (Kang et al., 2017) developed a

computer algorithm to identify Sill’s murmur in chil-

dren. They start with the development of an segmen-

tation algorithm to locate the ﬁrst and second heart

sounds, then they extracted signal features and after

they used machine learning-based classiﬁers, artiﬁcial

neural network and support vector machine to identify

Still’s murmur in children.

Delgado-Trejos et al. in (Delgado-Trejos et al.,

2009) used three families of features to present a com-

parison between them. They used time-varying and

time-frequency features, perceptual features and frac-

tal features. The results of each family of features

extracted were evaluated with a k-nearest neighbors

classiﬁer and they obtained better results when used

fractal features.

1.2 Overview Methodology

Our methodology starts by ﬁltering the heart sounds

and then we proceed to the feature extraction phase.

We have chosen to extract features of the time do-

main (mean, standard deviation and amplitude) and

MFCCs (Mel frequency Cepstral Coefﬁcients) and

the features extracted were normalized using a z-

score (McLeod, 2019). After this, seven machine

learning algorithms were used to make predictive

classiﬁcation models.

In our method the data from each auscultation spot

are processed separately. Only at the end the results

from each spot will be aggregated and a diagnosis

must be assigned. In this work, a patient is classi-

ﬁed as positive if a murmur is detected at least in a

single spot. Throughout this paper, spots will be re-

ferred according to the following acronyms: ’AV’ for

Aorta spot, ’MV’ for Mitral spot, ’PV’ for Pulmonary

spot and ’TV’ for tricuspid spot. The combinations

of spots are represented concatenating the combined

spot’s acronyms, like ’AVMVTV’ which means that

processes the Aortic, Mitral and the Pulmonary spots

simultaneously.

This paper has the following structure:

Section 2 Heart Sound: general concepts concern-

ing the heart sound are introduced.

Section 3 Methodology: this methodology used in

the experiments is presented in detail.

Section 4 Results: a summary of the results obtained

with the experiments are presented and discussed.

Section 5 Conclusion: the conclusions that may be

drawn from the experimental results are presented

together with the future work.

2 HEART SOUND PHYSIOLOGY

The vibrations and subsequent opening of the heart

valves caused by blood pressure during the cardiac

cycle, are the source of the cardiac sounds (Dorn-

bush and Turnquest, 2019). S1 sound of the heart is

produced when the mitral and tricuspid valves close

in systole and the S2 sound of the heart is produced

when the pulmonary and aortic valves close in dias-

tole (Dornbush and Turnquest, 2019). Systole occurs

between S1 and S2 and diastole occurs between S2

and S1. S1 and S2 are usually the events with the

A Multi-spot Murmur Sound Detection Algorithm and Its Application to a Pediatric and Neonate Population

229

highest amplitudes in a Phonocardiogram (PCG) sig-

nal (Figure 1) and have frequencies between 20-200

Hz (Naseri and Homaeinezhad, 2013).

Figure 1: PCG signals of normal patient (a) and patient with

murmur (b).

It is possible to distinguish heart murmurs due to

their longer duration. In pediatric age three types of

murmurs can be identiﬁed (Oliveira et al., 2013):

• Innocent Murmur: It usually happens in a well-

structured and functional heart;

• Functional or Physiological Murmur: Al-

though there is no cardiovascular abnormality,

there is a hemodynamic modiﬁcation that can al-

ter the normal blood ﬂow;

• Pathological or Organic Murmur: When func-

tional and structural abnormalities are present in

the cardiovascular system.

Generally, murmurs are caused by turbulent blood

ﬂow that can result in the narrowing or leaking of

heart valves or due to abnormal blood ﬂow in the heart

(Carvalho, 2018). According to the physiological sit-

uation that leads to the murmur, different sounds are

generated.

2.1 Auscultation Spots

The classic tool for assessing heart sounds is the

stethoscope (Dornbush and Turnquest, 2019). The

stethoscope can be used to auscultate the four heart

valves, being positioned in a speciﬁc area, according

to the Figure 2 to hear the desired valve: the aor-

tic valve is best heard in the second intercostal space

(right), just near to the sternum; the pulmonary valve

is best heard in the second intercostal space (left), just

near to the sternum; the tricuspid valve is best heard

in the fourth intercostal space (left) (parasternal line)

and the mitral valve is best heard in the ﬁfth inter-

costal space (left) (midline clavicular) (Dornbush and

Turnquest, 2019).

Figure 2: Cardiac auscultation spots.

3 METHODOLOGY

To solve the initial problem of this paper, it was de-

cided to build a methodology that follows a stan-

dard signal processing pipeline. Figure 3 shows the

scheme of our methodology.

Figure 3: Methodology scheme.

3.1 Data Set

The data set was collected during a screening cam-

paign entitled Caravana do Corac¸

ao. The screenings

were carried out in Brazil, in the state of Pernambuco,

in 2014 and 2015. As part of the protocol, all par-

ticipants completed sociodemographic questionnaires

and were analyzed with a clinical examination (phys-

ical and mental), screening (physiological measures)

and cardiac investigations (radiography, electrocar-

diogram and echocardiogram). In addition, electronic

auscultations were performed for each patient at four

of the main auscultation spots and an individual au-

dio sample was collected from each one for further

analysis. The data set used contains 687 patients, of

which 545 (70.3%) have a normal heartbeat and 142

(20.7%) have a cardiac disease. In relation to gender

the dataset has 399 male, 270 female patients and 18

fetus. The collected samples are from different age

groups. The average age is 5 ± 4 years old. The

youngest patient is a fetus, and the oldest one is 18

years old. The heart sounds were collected at 4000Hz

BIOSIGNALS 2021 - 14th International Conference on Bio-inspired Systems and Signal Processing

230

sampling rate. The dataset samples were segmented

by a cardiopneumologist who manually identiﬁed S1,

systole, S2 and diastole.

3.2 Pre-processing

Recorded data includes dispensable noise that can be

removed to improve feature extraction. To suppress

the interference of cardiac sound signals, it was nec-

essary to remove some of the frequencies. It is known

that the the spectral content of heart sound signals is

between 0 Hz and 200 Hz and the frequency of S1 and

S2 is normally between 30-80 Hz (Oliveira, 2018). So

we decide to cut frequencies above 200 Hz to remove

some of the noise. Therefore, the IIR ﬁlter Butter-

worth order 5 with a cut-off frequency of 200Hz was

used.

3.3 Feature Extracting

Based in (Liu et al., 2016), the following features

were extracted:

• Time-frequency Domain: Mean of S1 intervals,

Standard deviation of S1 intervals, Amplitude of

S1 intervals, Mean of systolic intervals, Standard

deviation of systolic intervals, Amplitude of sys-

tolic intervals, Mean of S2 intervals, Standard de-

viation of S2 intervals, Amplitude of S2 intervals,

Mean of diastolic intervals, Standard deviation of

diastolic intervals, Amplitude of diastolic inter-

vals.

• Perceptual Features: MFCCs (Mel-Frequency

Cepstrum Coefﬁcients): for the extraction of

MFCCs, we used a 25ms window and a 10ms step

and a total of 5 MFCC per window were calcu-

lated.

3.4 Classiﬁcation

To apply the Machine Learning (ML) algorithms, the

sets of sounds were previously divided into a training

and test set and also combined according to the car-

diac spot. After this, the features were divided three

times into a training and test set. To be divided into

a training and test set, initially all sounds with a heart

murmur were placed in one list and sounds without a

heart murmur in another list. For the training set, 70%

of the sounds were removed from the murmur list and

the other 70% from the list of non-murmur list, with

the remaining 30% of each list for the test set to be

possible to obtain the global distribution in the test set

and, with the training and test sets properly formed.

The following ML algorithms were used, with an ex-

haustive search over speciﬁed parameters values, to

make predictive classiﬁcation models: Support Vec-

tor Machine (SVM) (Evgeniou and Pontil, 2001), K

Nearest Neighbors (KNN) (Guo et al., 2003), Artiﬁ-

cial Neural Networks (ANN) (Zupan, 1994), Gradient

Boost (XGBoost) (Chen and He, 2014), Light Gradi-

ent Boost (LightGBM) (Ke et al., 2017), Random For-

est (RF) (Cutler et al., 2012) and Logistic Regression

(LogR) (Kleinbaum and Klein, 2010).

3.5 Decision Process

The ML models were constructed to make prediction

on samples. In order to make predictions on patients

we have combined the predictions, and a voting sys-

tem was used as follows: ”If at least one sound from

the patient is classiﬁed as a heart murmur, the pa-

tient has a heart murmur”. To assess the predictive

performance of the ML models some evaluation met-

rics were used. The metrics used to assess the per-

formance of the constructed models include the ac-

curacy, precision, recall and F1-Score. The results

in this paper are obtained from the test set in order

to assess the quality of the generalization and are fo-

cused on the F1-Score because it is one of the most

common metrics used for binary classiﬁcation in ma-

chine learning and the data set used have unbalanced

classes.

4 RESULTS

When analyzing the results obtained in general, an in-

crease in the number of spots used does not always

mean better results. The best results were obtained,

in almost all the algorithms used, in the combina-

tion of two spots, namely in the spots ’MVPV’ and

’PVTV’. The second best results were achieved with

three spots combinations.

In addition to this observation, the variation in the

results obtained with the algorithms when using only

one auscultation spot is greater than with the use of all

spots. In the Figure 4 we present the average of F1-

Score of all experiments and it is possible to observe

that the variability between algorithms is decreasing,

as more channels are used. This means that the im-

portance of the chosen algorithm decreases with the

increase in the number of spot.

In the Figure 5 it is possible to observe in detail the

difference in performance when a system with four

auscultation spots is used. The difference is lower

than 3%. In this case, we obtain the best result by

logistic regression with an average of performance of

89.9% and worst result by KNN with an average of

performance of 87.8%.

A Multi-spot Murmur Sound Detection Algorithm and Its Application to a Pediatric and Neonate Population

231

Figure 4: Average performance of all experiments with one,

two, three and four spots using F1-Score metric.

Figure 5: F1-Score in a four spot auscultation system with

its standard deviation.

In the Figure 6 the results obtained by the met-

ric F1-Score are found when the three auscultation

spots are used. When looking at the graph, more vari-

ability is found than when using the four auscultation

spots. The difference between the highest achieved

value and the lowest value is approximately 6%. In

addition to this observation, it appears that the high-

est value results are found with the combination of

’AVMVPV’ spot. With three spots, we obtain the best

result by logistic regression with an average of per-

formance of 89.8% and worst result by KNN with an

average of performance of 88.3%.

In the Figure 7 the results obtained by the metric

F1-Score are found when using the two auscultation

Figure 6: F1-Score in a three spot auscultation system with

its standard deviation.

Figure 7: F1-Score in a two spot auscultation system with

its standard deviation.

spot. Note that the values have greater variability than

with three and four spot. The difference between the

lowest and the highest value reached is approximately

9 %. It appears that the combination that obtained the

most valuable results was the ’MVPV’ combination,

followed by the ’PVTV’ combination. The best result

was obtained by the SVM with an average of perfor-

mance of 90.2% and worst result were obtained by

XGBoost with an average of performance of 87.9%.

In the Figure 8 the results obtained by the met-

ric F1-Score are found when using an auscultation

spot. When observing the variability between the al-

gorithms, it is noted that it is greater than with two,

three or four auscultation spots. The difference be-

BIOSIGNALS 2021 - 14th International Conference on Bio-inspired Systems and Signal Processing

232

tween the highest and lowest value is approximately

11%. With one spot, the best result was obtained by

the SVM with an average of performance of 88.9%

and worst result with KNN with average of perfor-

mance of 86.7%. The spot that obtained results with

higher values was the ’MV’ spot. Thus, it is possible

to conclude that the choice of the algorithm to be used

is more important with a smaller number of spot than

with a larger number of spot.

Figure 8: F1-Score in a one spot auscultation system with

its standard deviation.

5 CONCLUSION

The best results were obtained with two ausculta-

tion spots with logistic regression (with 91.1% of F1-

Score). When analyzing the results obtained, it can be

concluded that with the increase in the number of aus-

cultation spot in the experiments, the difference in re-

sults between the computational learning algorithms

used decreased, which means that the importance of

choosing an algorithm decreases with the increase of

number of auscultation spot used.

The F1-Score of all algorithms, when four aus-

cultation spots are used, contains approximate val-

ues. The average of the variance it is 0.005%. The

best result was obtained by logistic regression with

an average of F1-Score of 89.9%. When three aus-

cultation spots are used, the average of the variance

it is 0.012%. The best result was obtained by logis-

tic regression with an average of F1-Score of 89.8%.

When two auscultation spots are used, the average of

the variance it is 0.011%. The best result was ob-

tained by the SVM with an average of F1-Score of

90.2%. When one auscultation spot is used, the aver-

age of the variance it is 0.052%. The best result was

obtained by the SVM with an average of F1-Score of

88.9%. This fact may be an indicator that the choice

of the algorithm is more relevant when using only one

auscultation spot.

It was found that the best values obtained in

the results of most algorithms corresponded to the

’MVPV’. Are these the most important spots for doc-

tors? To answer this question, we have requested a

student from medicine to analyze a sample (81 pa-

tients) of our data set, not only she veriﬁed the pres-

ence of heart murmurs, but she also identiﬁed the

spots on which the murmurs are most audible, their

frequency by auscultation spot is displayed on Table

Table 1: Number of times where the spot was the most au-

dible.

AV MV PV TV

13 20 31 17

In this sense, there is a hypothesis that when com-

bining spot where the murmur is more audible, better

results are obtained than if spot are used where the

murmur is not so audible. It is observed that the spot

that appears less often as the most audible spot is the

’AV’ spot and when combinations are made with this

spot, lower results are obtained. There is a possibility

that, in children, the ’MV’, ’PV’, ’TV’ spots are the

most important in view of the pathologies of the study

population.

ACKNOWLEDGMENTS

The authors would like to acknowledge the Mestrado

Integrado em Engenharia Inform

atica e Computac¸

(MIEIC), Faculdade de Engenharia da Universidade

do Porto (FEUP).

This work was also supported by the DigiScope2

project (POCI-01-0145-FEDER-029200-PTDC/CCI-

COM/29200/2017), funded by Fundo Europeu de De-

senvolvimento Regional (FEDER), through Programa

Operacional Competitividade e Internacionalizac¸

(POCI).

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