A multi-class heartbeat classifier employing hybrid fuzzy -neural network

A major concern of the medical faculty is the onset of the rapidly increasing heart disease cases. Prevention of the heart diseases is one of the most trusted methods in curbing this problem. Electrocardiogram (ECG) diagnosis has proved to be a very effective means of studying the condition of the heart and thus its present state in addition to being inexpensive and non-invasive technique. A major problem of long term or ambulatory ECG is that large number of heartbeats is recorded and manually studying them and thus classifying them as belonging to certain cardiac problems is a time consuming task also prone to human errors. A still grievous situation exists where cardiac experts are not available easily, especially in remote areas (a typical scenario in developed and under-developed countries). In this paper the authors have proposed a novel strategy for automatic heartbeat classification to palliate the above mentioned problems. Ten types of heartbeats considered for automatic classification are atrial premature contraction (APC), fusion(F), left bundle branch block type I and type II (LBBBB I& LBBBB II), normal(N), paced(P), right bundle branch block type i and type II (RBBBB I & RBBBB II) , premature ventricular contraction type I and type II ( PVC I & PVC II). Fuzzy c-means clustering (FCM) is employed for feature extraction of the individual ECG cycles and these extracted features are then used for training multilayer perceptron. A detailed study has been undertaken to find the optimum number of clusters and optimal MLP configuration with the metric of overall percentage classification accuracy. The best FCM-MLP topology exhibited overall classification accuracy of 98.25%. This network was tested for performance in presence of additive white Gaussian noise and was found to be very robust. For comparison, a well-known method of principal component analysis (PCA) was also experimented with. FCM-MLP performs better than PCA-MLP in classifying the - - correct heartbeats.