CLASSIFICATION AND DETECTION OF DYSRHYTHMIA FOR LECTROCARDIOGRAPHY SIGNALS BY CONVOLUTIONAL NEURAL NETWORK

Abstract

Electrocardiography is the most useful method for diagnosing cardiovascular disease such as arrhythmia. Heartbeat classification of electrocardiography signals is a valuable and hopeful technology for early warning of dysrhythmia because it contains the cardiac electrical activities and reflects the abnormal cardiac activity. Therefore, a novel electrocardiogram-based arrhythmic beats classification was proposed to automatically detect the main types of dysrhythmia using electrocardiography signal in this work. A convolutional neural network model was used to automatically detect the normal and the types of dysrhythmia electrocardiography beats. The beat was transformed into a matrix as two-dimensional input to the model. The classification system was assessed to detect the normal, left bundle branch block, premature ventricular contraction and right bundle branch block beats using the MIT-BIH arrhythmia database. The results showed that an average accuracy of 99.30% and 98.85% was achieved by using ML2 lead of electrocardiography data with one-dimensional and 2-dimensional input, respectively. An average accuracy of 97.00% and 97.20% was achieved by using V1 lead of electrocardiography data with one-dimensional and 2-dimensional input, respectively. Moreover, no feature extraction of signals was carried out in this study. Consequently, the proposed model can accurately test the unknown electrocardiography signal and aid the clinician in the diagnosis of dysrhythmia.