A high-precision arrhythmia classification method based on dual fully connected neural network

As an important arrhythmia detection method, the electrocardiogram (ECG) can directly reflect abnormalities in cardiac physiological activity. In view of the difficulty in the diagnosis of arrhythmia in different people, automatic arrhythmia detection methods have been studied in previous works. In this paper, we present a dual fully-connected neural network model for accurate classification of heartbeats. Our method is following the AAMI inter-patient standard, which includes normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). Firstly, a total of 105 features are extracted from the preprocessed signals. Then, a two-layer classifier is introduced in the classification stage. Each layer contains two independent fully-connected neural networks, and the threshold criterion is also added in the second layer. For verification, both the MIT arrhythmia database (MITDB) and the MIT supraventricular arrhythmia database (SVDB) were adopted. The experiments demonstrate that the proposed method has high performance for arrhythmia detection. It also achieves high sensitivity for class S and V, which can easily detect potentially abnormal heartbeats. Furthermore, the proposed method can interfere with the classification effect for a certain disease and have more advantages in dataset size when comparing a convolutional neural network (CNN). Once properly trained, the proposed method can be employed as a tool to automatically detect arrhythmia from ECG.