Classification of Cardiac Arrhythmias Based on Artificial Neural Networks and Continuous-in-Time Discrete-in-Amplitude Signal Flow

Conventional Artificial Neural Networks (ANNs) for classification of cardiac arrhythmias are based on Nyquist sampled electrocardiogram (ECG) signals. The uniform sampling scheme introduces large redundancy in the ANN, which results high power and large silicon area. To address these issues, we propose to use continuous-in-time discrete-in-amplitude (CTDA) sampling scheme as the input of the network. The CTDA sampling scheme significantly reduces the sample points on the baseline part while provides more detail on useful features in the ECG signal. It is shown that the CTDA sampling scheme achieves significant savings on arithmetic operations in the ANN while maintains the similar performance as Nyquist sampling in the classification. The proposed method is evaluated by MIT-BIH arrhythmia database following AAMI recommended practice.