Cardiac state diagnosis using adaptive neuro-fuzzy technique

Abstract

Analysis of heart rate has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). These signals may either contain indicators of a current disease or even warnings about impending diseases. However, to manually study and pinpoint heart abnormalities in voluminous data is strenuous and time consuming. Here, an adaptive neuro-fuzzy network is used to classify heart abnormalities in 10 different cardiac states and shown to be effective. The results indicate a high level of efficacy of tools used with an accuracy level of more than 94%.