Heart Sounds Classification for a Medical Diagnostic Assistance

Abdelhamid Bourouhou,Abdelilah Jilbab,Chafik Nacir,Ahmed Hammouch

doi:10.3991/ijoe.v15i11.10804

Abstract

<span lang="EN-US">In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs) with its different types of kernels (i.e.; Linear, polynomial and MLP). The best classification accuracy obtained was 88.25%, using the SVM classifier with an MLP kernel.</span>

Highlights

Heart disease is the leading cause of death in the world
We tried the Support Vector Machines (SVMs) classifier in same way as the Generalized Linear Model (GLM) classifier previously presented, but with parameters default Multi-Layer Perceptron (MLP) [1 -1], after we tried the same classification by modifying these MLP parameters each time until we concluded that the [5 -5] are the best parameters to be adopted
The SVM classifier is better than GLM classifier

Summary

Introduction

Heart disease is the leading cause of death in the world. A published study says, these diseases represent a third of all deaths in 2015, with nearly 18 million deaths from cardiovascular disease in the world [1]. Statistics show that the mortality rate caused by cardiovascular disease is growing continuously, and there were nearly 13 million deaths from cardiovascular disease in 1990, to 17,92 million in 2015. These diseases are a set of disorders affecting the heart and blood vessels which includes coronary heart disease, cerebro-vascular, rheumatic heart disease and other affections. An early detection of signs of cardiac abnormality represents an interesting initiative to take care of the patient [2]

Methods

Results

Conclusion