Automated Arrhythmia Detection using Hilbert-Huang Transform based Convolutional Neural Network

In this paper, a novel approach to arrhythmia-based signal classification is introduced. The objective is to properly identify three classes of patients exhibiting normal sinus rhythm, atrial fibrillation, and other rhythm. The proposed method apply Hilbert-Huang transform on raw signal to generate noise-free reconstruction of the original containing temporal variations as input for classification mechanism to learn representative features. The features are directly learned by Convolutional Neural Network, thus replacing traditional methods of relying on experts to handcraft features. To summarize, this paper contains two major processes: utilize a nonlinear and nonstationary signal processing technique to produce input, and to feed reconstructed signal containing representative features to CNN for multi-classification task. The experimental results indicate the effectiveness of this method, removing the need of human involvement in the process of feature selection. Through analyses and stimulations, the effectiveness of the proposed ECG-classification method is evaluated.