Abstract
This paper aims at bringing a scientific contribution to the cardiac arrhythmia biomedical diagnosis systems; more precisely to the study of the amelioration of cardiac arrhythmia classification performance using artificial neural network, adaptive neuro-fuzzy and fuzzy inference systems classifiers. The purpose of this amelioration is to enable cardiologists to make reliable diagnosis through automatic cardiac arrhythmia analyzes and classifications based on high confidence classifiers. In this study, six classes of the most commonly encountered arrhythmias are considered: the Right Bundle Branch Block, the Left Bundle Branch Block, the Ventricular Extrasystole, the Auricular Extrasystole, the Atrial Fibrillation and the Normal Cardiac rate beat. From the electrocardiogram (ECG) extracted parameters, we constructed a matrix (360 × 360) serving as an input data sample for the classifiers based on neural networks and a matrix (1 × 6) for the classifier based on fuzzy logic. By varying three parameters (the quality of the neural network learning, the data size and the quality of the input parameters) the automatic classification permitted us to obtain the following performances: in terms of correct classification rate, 83.6% was obtained using the fuzzy logic based classifier, 99.7% using the neural network based classifier and 99.8% for the adaptive neuro-fuzzy based classifier. These results are based on signals containing at least 360 cardiac cycles. Based on the comparative analysis of the aforementioned three arrhythmia classifiers, the classifiers based on neural networks exhibit a better performance.
Highlights
The rapid evolution of heart diseases is increasingly becoming a major health challenge in the world today
In efforts to diagnose these diseases, cardiologists face diverse difficulties in showing a reliable diagnosis of patients suffering from cardiac arrhythmias
Different groups of researchers in an attempt to solve the problem have worked on automatic classification methods in order to obtain
Summary
The rapid evolution of heart diseases is increasingly becoming a major health challenge in the world today. This is demonstrated by the high and increasing mortality rate resulting from heart related diseases [1]. In efforts to diagnose these diseases, cardiologists face diverse difficulties in showing a reliable diagnosis of patients suffering from cardiac arrhythmias. They are hindered by the presence of noises that disturb the phase and amplitude characteristics of the signal. These noises come from processes other than heart muscles. Different groups of researchers in an attempt to solve the problem have worked on automatic classification methods in order to obtain
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More From: International Journal of Healthcare Systems Engineering
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