Deep convolutional neural network application to classify the ECG arrhythmia

The ECG signal is such a substantial means to reflect all the electrical activities of the cardiac system. Therefore, it is considered by the physician as the essential tools and materials to diagnose and treat heart diseases. To deal with different types of arrhythmia, the physician manually inspects the ECG heartbeat. Since there are tiny alternations in the amplitude, durations and therefore the morphology, the computer-based systems were needed to develop such solutions in order to help the physician to do their job. In this study, a novel tactic to automatically classify ten different arrhythmia types was developed depending on the deep learning theory. Consequently, the well-known convolutional neural network (CNN) approach was adopted to classify those different types of arrhythmia. The structure of the proposed model consists of 11 layers distributed as follows: four layers as convolution interchanged with other four layers of max pooling and finally three successfully connected layers. The experiment was conducted with the dataset which was downloaded from the Physionet in the Massachusetts Institute of Technology-Beth Israel Hospital database and then augmented to get sufficient and balanced dataset. To evaluate the performance of the proposed method and compare it with the previous algorithms, confusion matrix, sensitivity (SEN), specificity (SPE), precision (PRE), area under curve and receiver operating characteristic have been used and calculated. It has been found that performance from the proposed method is better than the existing methods based on CNN, and the accuracy is 99.84.