Deep classifier on the electrocardiogram interpretation system

Abstract

Electrocardiogram (ECG) is a primary diagnostic tool for cardiovascular diseases. A higher accuracy of heart diseases needs an automatic classification for intelligent interpretation of cardiac arrhythmia. The classification process consists of following stages: detection of QRS complex in ECG signal, feature extraction from detected QRS using R-R interval, segmentation of rhythms using extracted feature set, learning system by using Deep Neural Networks (DNNs). The performance is analyzed as a rhythm of arrhythmia classifier and MIT-BIH arrhythmia database uses to validate the method. To benchmark, the performance of DNNs algorithm is compared to, MLP and SVM algorithm in terms of accuracy. The result obtained show that the proposed method provides good accuracy about 97.7 % with less expert interaction.