A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression

Arrhythmia classification is crucial in electrocardiogram (ECG) based automatic cardiovascular disease diagnosis, e.g., to help prevent stroke or sudden cardiac death. However, the complex individual differences in ECG morphology make it challenging in accurately categorizing arrhythmia heartbeats. To promote robustness of the algorithm for individual differences, we propose a novel ECG arrhythmia classification method with stacked sparse auto-encoders (SSAEs) and a softmax regression (SF) model. The SSAEs is employed to hierarchically extract high-level features from huge amount of ECG data. Features are extracted automatically such that no individual difference in feature selection will bias extraction accuracy. Moreover, the input can be reconstructed completely by the features in each level of the auto-encoder. The SF is then trained to serve as a classifier for discriminating six different types of arrhythmia heartbeats. Computational experiments and comparative analyses are presented to validate the effectiveness of the theoretical models.