Automated Rheumatic Heart Disease Detection from

Phonocardiogram in Cardiology Ward

Melkamu Hunegnaw Asmare

1,2,3

, Frehiwot Woldehanna

, Luc Janssens

and Bart Vanrumste

1,2

KU Leuven, Campus Group T, eMedia Research Lab, Leuven, Belgium

KU Leuven, Electrical Engineering Department (ESAT), STADIUS, Leuven, Belgium

Addis Ababa Institute of Technology, Center of Biomedical Engineering, Addis Ababa, Ethiopia

Keywords: Rheumatic Heart Disease, Machine Learning, Support Vector Machines, Phonocardiogram.

Abstract: Rheumatic Heart Disease (RHD) is a preventable and treatable form of cardiovascular diseases. It is also re-

ferred to as the ailment of the disadvantaged mainly affecting children and young adults. RHD is recognized

as a global health priority by World Health Organization. This chronic heart condition silently deteriorates the

normal function of the heart valves which can be detected as a heart murmur using a stethoscope. As the

cardiac auscultation process is an elusive process, the clinician will always be tempted to refer the patient for

expensive and sophisticated imaging procedures like echocardiography. In this study, a machine learning

algorithm is developed to augment the limitation in the auscultation process and transform the stethoscope as

a powerful screening tool. For this current study, an RHD heart sound data set is recorded from one hundred

seventy subjects. A total of twenty-six features are extracted to model murmur due to RHD. Twenty-four

classification and regression algorithms have been tested out of which the Cubic SVM has demonstrated su-

periority with a classification accuracy of 97.1%, with 98% sensitivity, 95.3 % of specificity 97.6% precision.

The corresponding positive predictive values (PPV) are 96% and 97% for normal and RHD respectively. The

results are based on data collected from a cardiology ward where there are more pathological cases than con-

trols. Hence it is a valuable detection tool in a cardiology clinic. But in the future, integrating this machine

learning algorithm with a mobile phone can be a powerful screening tool in places where access to echocar-

diography and cardiologist is difficult. Thus, it can then aid a timely, affordable and reliable detection tool

allowing a non-medically trained individual to screen and detect RHD.

INTRODUCTION

Due to the rapid epidemiological transition observed

in developing countries, not only communicable dis-

eases but also noncommunicable diseases are

becoming the major cause of death risks.

Cardiovascular diseases, cancer, chronic respiratory

disorders, and diabetes are the most common ones.

Among these, cardiovascular disorder takes the

leading role (WHOAnnualReport, 2013). Worldwide,

ischemic heart disease is the number one cause of

death, which affects males with age usually 65 or

more (Emelia J. Benjamin, 2019). However, RHD is

the leading cause of cardiovascular disorders in

middle- and low-income countries. The average age

is around 28 years with females affected twice as

much as men (Watkins DA, 2017).

RHD is caused by Group A streptococcal

Bacteria (GAS) infection. These bacteria are

normally found in the skin and in the throat of healthy

people. GAS is an important cause of throat infection.

In certain susceptible people, usually children, the

immune system becomes confused and attacks both

the GAS bacteria and parts of the host’s body. This

autoimmune at- tack causes the inflammation of the

joints, skin, brain and most importantly the heart

(Watkins DA, 2017). RHD is a chronic heart

condition and early in the dis- ease, there are usually

no symptoms. The disease can silently progress

especially after repeated episodes of infection. Each

episode brings renewed heart valve inflammation that

eventually leads to local scarring and distortion of the

valve architecture. First, the affected valve starts to

leak, normally referred to as re- gurgitation; later the

scarring can stop the valve from opening properly

and make it narrow for sufficient blood passage,

referred to as stenosis. These abnormalities create

unusually turbulent blood flow in the heart chambers

Asmare, M., Woldehanna, F., Janssens, L. and Vanrumste, B.

Automated Rheumatic Heart Disease Detection from Phonocardiogram in Cardiology Ward.

DOI: 10.5220/0009367108390844

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 839-844

ISBN: 978-989-758-398-8; ISSN: 2184-4305

839

which is called heart murmur. Left untreated, RHD

will compromise the cardiac output of the patient

which will subsequently lead to pre- mature death

(Walsh, 2019). The heart sound wave- form has

distinct features called the first heart sound (S1), the

second heart sound (S2), systole and diastole parts.

Murmur normally presents itself in the systolic or

diastolic parts. The heart sound can be listened to

and recorded using a stethoscope in the form of a

phonocardiograph (PCG). A three second MATLAB

plot of clean, noisy and murmur types of heart sound

is shown in Fig. 1. The clean signal is manually

segmented to locate S1, S2, systole and diastole.

Figure 1: Time series representation of heart sounds: clean

heart sound with S1, S2, Systole and Diastole labelled (top),

noisy heart sound (middle), and heart sound with a murmur

(bottom).

The heart sound gives vital information about

cardiac wellbeing. However, even under ideal condi-

tions, the accuracy of diagnosis is very low (Pelech,

2004). This is in reality attributed to the inherent

limitation of the human auditory system to perform

accurate auscultation. On top of that, the listening

process is highly subjective. This usually forces

doctors to be highly dependent on other expensive

imaging devices like echocardiography and x-ray for

cardiac screening (Vukanovic-Criley JM, 2006).

To counter the subjectivity and the high percent-

age of diagnostic errors, computer-aided diagnostic

(CAD) systems can provide paramount importance

(Bozˇo

Tomas,

2007), (Belloni

and

Spoletini,

2007).

For the successful implementation of CADs, the qual-

ity of the input signal should be high. Such automa-

tion has been researched for over six decades now. In

the 1960s, one of the ground-breaking studies in the

automatic classification of heart sound pathology was

performed by (D. S. Gerbarg and Hofler, 1963). Since

then thousands of research papers have been

published. Some of the prominent works have been

properly investigated in the report published in 2016

by (Liu C1, 2016). This paper demonstrates the im-

portance of a well-characterized dataset for develop-

ing successful classification algorithms. This work

also assembles the largest heart sound dataset.

The 2016 PhysioNet Computing in Cardiology

Challenge was one of the most successful challenges

conducted by the program which attracted a large

number of researchers to solve the heart sound classi-

fication to normal and abnormal. In the competition,

the largest heart sound dataset compiled by (Liu C1,

2016) was provided. The winners of the competi-

tion, Potes et al. (Cristhian Potes, 2016) have devel-

oped a deep-learning-based classifier that combines

time-frequency features with a reported sensitivity of

96%, specificity of 80% and overall accuracy of 89%.

Almost all previously proposed algorithms needed

the segmentation of the heart sound recording into

first heart sound, second heart sound, systole, and di-

astole parts. This is a reasonable assumption which

may lead to pinpointing of abnormalities in the heart

sounds at specific temporal locations. However, the

complexity and also the error introduced in the accu-

rate localization of the segments have decreased the

performance of the algorithms.

Recently, P. Langley and A. Murray (Cristhian

Potes, 2017) have demonstrated the feasibility of

accurate classification without segmentation of the

heart sounds. The paper has a relatively lower overall

accuracy of 79% (specificity 80%, sensitivity 77%)

classification, and claims this is mainly due to the

quality of the dataset used. Despite the sheer volume

of research done in the area, the studies are critically

hampered by the lack of high- quality recordings that

have proper validation and standardization. This

would have created common formatting that allows

collaborative research, large- scale analytics, and

tools and methodologies to be shared. The largest

available open access data set is available which was

compiled by Liu et al. (Liu C1, 2016). It contains 2435

heart sound recordings from 1297 subjects. The

dataset consists of recordings from subjects with a

variety of abnormalities which include heart valve

damage and coronary artery disease. The maximum

overall accuracy reported in the literature by using this

database is only 94% which was achieved by

introducing different model optimization techniques

(Suhm, 2019).

D.B. Springer et al. (D.B. Springer, 2014) have

worked on a dataset that is recorded to classify an

RHD from normal heart sounds. A total of 318

recordings from 106 subjects where 40 were identi-

fied with RHD. Their aim was to detect systolic mur-

mur hence the heart sound is segmented before feature

extraction. A combination of MFCC and wavelet fea-

tures are used. SVM classification algorithm is used

by optimizing its parameters and the procedure is

validated using a 10-fold cross-validation technique.

Cognitive Health IT 2020 - Special Session on Machine Learning and Deep Learning Improve Preventive and Personalized Healthcare

840

They reported a maximum F1 score of only 0.7, the

sensitivity of 74.8% and specificity of 74.5%. Poor

quality recording and external generator noise were to

blame for such low performance. This demonstrates

the necessity for a large and reliable dataset which

takes into account specific pathology.

For the current study, we gathered one of the

largest available heart sound data set by recording it

from one hundred seventy subjects with a state-of-the-

art electronic stethoscope and addresses a particular

type of valvular heart disease called RHD. Twenty-

six different features are used and the features are

computed from a non-segmented data. These features

include time-domain components, frequency compo-

nents, and perceptual components. These features are

extracted from the entire signal to properly deal with

systolic as well as diastolic murmurs.

MATERIALS AND METHODS

2.1

Data Collection

The heart sound data was collected at Tikur Anbessa

Referral Teaching Hospital, College of Health Sci-

ences, Addis Ababa University, Addis Ababa,

Ethiopia from August 2018 to July 2019. The study

protocol was approved by the Research Ethics Com-

mittee of the Department of Internal Medicine (Ethi-

cal Clearance No: 014/2018).

The heart sound data were recorded from one

hundred seventy subjects, one hundred twenty four

were confirmed RHD patients (seventy four females,

fifty males) with ages from 9 to 47 with mean and SD

of 22.9±8.9 years. The time since the first diagnosis

is from two months to 20 years with mean and SD of

3.3±3.1 years. Each diagnosis is confirmed by

echocardiographic imaging and a cardiologist

analysis. There were 46 normal subjects (15 females,

31, males) with age 5 to 37 years with mean and SD

of 14.4±10.5 years. The electronic heart sound was

recorded by ThinklabsOne

digital stethoscope with

a sampling frequency of 44.1KHz. An average of 3.88

minutes of recording per subject is acquired. The

digital stethoscope was positioned at the fifth

intercostal space around the midclavicular line to

properly record the mitral valve sound. The audio data

is transferred to a mobile phone and is saved as a wav

file.

2.2

Visualization

A ten-second waveform of a normal heart sound from

a healthy subject is shown in Fig. 2 (top). In this fig-

ure, the S1 and the S2 are clearly identifiable. Fig. 2

(bottom) shows the corresponding spectrogram to vi-

sualize how the energy is distributed over time. Clicks

and glitches which are common features of a murmur

can be very well visualized in the spectrogram.

Figure 2: Healthy Subject: Time domain wave from (top)

and spectrogram in Mel-frequency scale shown thermal col-

ormap

Instead of a linear scale, the Mel- frequency scale

is used to roughly resemble the resolution of the hu-

man auditory system. The spectrogram in the Mel-

frequency scale shows much of the spectral density

that belongs to S1 and S2 whereas systole and dias-

tole durations contain very little concentration. Fig. 3

shows is a recording from an RHD patient. In this

recording, S1 and S2 can still be easily identified.

However, looking at the corresponding spectrogram,

it can be seen that there is a significant amount of en-

ergy in the systole and diastole parts which indicates

the presence of murmur.

Figure 3: RHD Patient: Time domain wave from (top) and

spectrogram in Mel-frequency scale shown thermal color

map.

2.3

System Architecture

This section presents the complete workflow of devel-

oping a machine learning application to automatically

detect RHD. The data acquisition, pre-processing,

feature extraction, and classification steps are pre-

sented in detail. The classification performance of

various classification algorithms is also investigated.

Automated Rheumatic Heart Disease Detection from Phonocardiogram in Cardiology Ward

841

The overall architecture of the system is shown in

Fig. 4. Detail explanation of all the steps is presented

below.

Figure 4: Overall System Architecture.

2.3.1

Pre-processing

The original data is sampled at 44.1kHz. Experiments

conducted at a sampling rate greater than or equal to

2kHz showed that the performance of the classifica-

tion algorithm is not significantly affected. Hence, the

data is down sampled to 2kHz. Each record is labelled

as Normal and RHD based on echocardiography anal-

ysis and cardiologist decision. The records are split to

more manageable windows of 5 seconds and are ready

for feature extraction. As the data is recorded in an

uncontrolled environment and many of the record-

ings are corrupted by various types of noises such as

movement artefact, talking, mobile phone inter-

ference, traffic sound, coughing, lung sounds, gas-

trointestinal sounds, pounding and clicks due to high

volume recording. However, no filtering or noise re-

moval was done on the data to make sure that the sys-

tem resembles a real practice scenario.

2.3.2

Feature Extraction

In RHD patients, auscultation reveals the

characteristic systolic murmur of mitral regurgitation.

When the disease progresses, an additional diastolic

murmur may also be present. The intensity of the

murmur generally correlates with the severity of the

disease

(Liesl

Zu¨hlke,

2019).

Different

representations

the recorded heart sound are required to detect these

abnormalities. In the literature, several features are

proposed in order to properly characterize murmur

due

RHD

(Zu¨mray

Dokur,

2009).

These

features

are extracted from the entire signal to properly deal

with systolic as well as diastolic murmurs. Finally,

twenty six features were extracted which include

Time Domain Features (Median, Standard Deviation,

Mean Absolution Deviation, 25

percentile, 75

percentile, Inter Quartile Distance, Skewness,

Kurtosis), Frequency Domain Features (Shannon’s

Energy, Spectral Entropy, Dominant Frequency,

Energy Magnitude at the Dominant Frequency,

Dominant Frequency Ratio), and Perceptual Features

which first thirteen elements of the Mel frequency

cepstral coefficients (MFCC). Table 1 presents the

formulas and descriptions used in computing the

above features.

2.3.3

Classification

There exist several machine learning algorithms spe-

cially tailored for predictive modeling. Selecting the

best one is usually a tradeoff between speed of train-

ing, memory usage and predictive accuracy on new

data. In medical applications, not only the accuracy

but also specificity and sensitivity are also important.

Several existing classification algorithms are exper-

imentally compared to our heart sound dataset col-

lected from one hundred seventy subjects. Support

Vector Machines (SVM), K-Nearest Neighbor (KNN)

classification algorithms have found to classify the

data with superior accuracy.

2.3.4

Validation

The classification performance of the different algo-

rithms is compared by the overall accuracy, sensitiv-

ity, specificity and F1-Score on the 30% holdout val-

idation data. The confusion matrix is also computed.

Table 2 presents the validation parameters and corre-

sponding formulas.

RESULTS

Two experiments are conducted to evaluate the

classification performance of Cubic SVM and Fine

KNN. The experimental results are shown in Table 3.

3.1

Experiment I

The first experiment is done on our collected heart

sound data. This set has a total of 170 subjects (124

confirmed RHD patients and 46 Normal Subjects). A

total of 3957 (2886 RHD and 1071 Normal) records

with a 10-second duration are used. This data is

further split into 5-second intervals as mentioned in

the pre-processing step. The data is recorded in an

uncontrolled environment and many of the record-

ings are corrupted by various types of noises such as

movement artefact, talking, mobile phone interfer-

ence, traffic sound, coughing, lung sounds, gastroin-

testinal sounds, pounding and clicks due to high vol-

ume recording. No pre-processing or removal of the

data was done to simulate an actual auscultation en-

vironment. For this setup, Cubic SVM has an over-

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842

Table 1: Formulas and description to compute features.

Feature

Formula /Description

Median

Standard Deviatio

Mean Absolute

deviation

mean.dev(x)

=−



X is the heart sound Signal

Skewness

Kurtosis

Shannon Entropy

Spectral Entropy

Dominant

Frequency

The frequency at which the

maximum of the spectrum occurs

Dominant

Frequency ratio

The ratio of the energy of the

maximum to the total energy of

the signal

Mel-frequency

Cepstral

Coefficients

The steps followed are:

preemphasise heart sound

signal using a first-order FIR filter

with preemphasis coefficient of

0.97.

Computer short-time Fourier

transforms.

Magnitude spectrum com-

putation followed by filter bank

design with 20 triangular filters

uniformly spaced on the Mel scale

between 10Hz and 700Hz to span

the standard frequency range of

heart sounds.

The filter bank is applied to

the magnitude spectrum values to

produce filter bank energies

(FBEs) (20 per frame)

Log-compressed FBEs are

then decorrelated using the

discrete cosine transform to

produce cepstral coefficients.

apply sinusoidal lifter to

produce liftered MFCCs.

all accuracy of 98.5% with 97.4% sensitivity,

99.0% specificity, and 98.1% precision. The

corresponding PPV is 98% for normal and 99% for

RHD.

Table 2: Validation Parameters.

Paramete

Formula

Accuracy

T p

Sensitivity

T p

F p

Specificity

F p

Precision

T p

F p

3.2

Experiment II

It is noted that both the training and validation sets are

unbalanced. This can only demonstrate a clinical

setup where there are more positives than negatives.

This second experiment is done to check the ro-

bustness of the system against noise and different

recording environments. For this purpose, 231 clean

heart sound recordings from normal subjects and 120

heart sound recordings with a substantial amount of

background noise and distortion are included from the

Bentley et al. (Bentley et al., 2011) open dataset.

These data are collected using iStethoscope ProTM

iPhone app and DigiScopeTM from the general

public and clinical trials. A total of 4308 (2886 RHD

and 1422 Normal) records with a 10-second duration

is used for this experiment. The data is further split

into a 5-second duration as mentioned in the pre-

processing step. The Cubic SVM algorithm has again

proved to be resilient to the noise by achieved 97.1%

accuracy with 98% sensitivity, 95.3% of specificity

and 97.6% precision. The corresponding PPV values

are 96% for normal and 97% for RHD.

Table 3: Experimental Results.

DISCUSSION AND

CONCLUSIONS

Although heart sounds can actually tell you a lot about

a patient, auscultation proficiency is declining fast.

This is mainly due to the intrinsic weakness of the

human auditory system and the technical ability gap

in which a clinician can explain why I need to know it

where there is more advanced technology to do it. Due

to this instead of auscultation, echocardiography is

considered as a gold standard to screen and diagnose

valve damage in the heart. Unfortunately, in

developing countries, such an advanced device is very

expensive and cardiologists who can use it are rare.

Automated Rheumatic Heart Disease Detection from Phonocardiogram in Cardiology Ward

843

To make matters worse, the burden of cardio-vascular

disease due to the non-curable but treatable and

preventable rheumatic heart disease is very high in

these countries. The damage to the heart valves due to

RHD can be reduced if it is detected early. The most

straightforward and cheapest approach to detect

valvular damage is detected by listening to the heart

murmurs. In this study, one of the largest heart sound

dataset is collected. Using this dataset, a compre-

hensive machine learning method is deployed. This

study has demonstrated the performance of the ma-

chine learning algorithm with extensive characteriza-

tion methods to quantify and accurately classify heart

sounds of a normal and confirmed Rheumatic Heart

Disease. A total of 26 features that encompass time

domain, frequency domain, and perceptual character-

istics are carefully selected and computed. The results

of the current study are very promising with respect to

classification accuracy of 97.1 %, with 98 % sensitiv-

ity, 95.3 % specificity, and 97.6% precision and a pos-

itive predictive value of 99% in detecting RHD. Our

data depicts better results in terms of classification ac-

curacy, sensitivity, and specificity than previously re-

ported studies available on heart sound data. While

this study considers a specific type of heart disease,

the other studies were trying to model generic types

of heart diseases.

Furthermore, we hope that this technology cou-

pled with mobile phone devices can be used as a

screening tool in a clinical environment where access

to echocardiography and cardiologist is difficult. This

will make it a timely, affordable and reliable detec-

tion tool allowing a non-medically trained individual

to diagnose and screen for RHD.

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