TELKOMNIKA Telecommunication, Computing, Electronics and Control
Classification of heart disease based on PCG signal using CNN

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Classification of heart disease based on PCG signal using CNN

Continuous wavelet transform
Convolutional neural network
Heart disease
Phonocardiogram

Cardiovascular disease is the leading cause of death in the world, so early
detection of heart conditions is very important. Detection related to
cardiovascular disease can be conducted through the detection of heart
signals interference, one of which is called phonocardiography. This study
aims to classify heart disease based on phonocardiogram (PCG) signals using
the convolutional neural networks (CNN). The study was initiated with
signal preprocessing by cutting and normalizing the signal, followed by a
continuous wavelet transformation process using a mother wavelet analytic
morlet. The decomposition results are visualized using a scalogram, then the
results are used as CNN input. In this study, the PCG signals used were
classified into normal, angina pectoris (AP), congestive heart failure (CHF),
and hypertensive heart disease (HHD). The total data used, classified into 80
training data and 20 testing data. The obtained model shows the level of
accuracy, sensitivity, and diagnostic specificity of 100%, 100%, and 100%
for training data, respectively, while the prediction results for testing data
indicate the level of accuracy, sensitivity, and specificity of 85%, 80%, and
100%, respectively. This result proved to be better than the mother wavelet
or other classifier methods, then the model was deployed into the graphical
user interface (GUI).

Aditya Wisnugraha Sugiyarto, Agus Maman Abadi, Sumarna

http://journal.uad.ac.id/index.php/TELKOMNIKA

Jul 28, 2021

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