Error-rate analysis for ECG classification in diversity scenario

This paper proposes with error-rate for an ECG classifier to support doctors in clinical diagnosis. In particular, ECG signals, which are diversity, may be obtained from various ECG machines on many patients. Therefore, ECG classification has been a critical challenge for scientists in recent years. In the previous studies, the ECG classification produced a promised precision. However, diversity phenomenon on patients had not been considered yet to be able to increase precision. In this paper, a performance of a state-of-art ECG-classifier in diversity scenario will be analyzed using error-rate. In this research, training and testing data are arranged to be different on MIT dataset. Thus, ECG data are separated and then extracted into each heartbeat using a discrete wavelet transform algorithm. Extracted features are trained using a state-of-art neural network method in diversity and non-diversity scenarios. Experimental results demonstrated that the accuracy of the separated data is higher using the ECG classification.