Automatic Classification of CAD ECG Signals With SDAE and Bidirectional Long Short-Term Network

Abstract

Coronary artery disease (CAD) has been one of main causes of heart diseases globally. The electrocardiogram (ECG) is a widely used diagnostic tool to monitor patients' heart activities, and medical personnel need to judge whether there are abnormal heartbeats according to captured results. Therefore, it is significant to identify ECG signals accurately and fast. In this paper, a fast and accurate electrocardiogram (ECG) classification system based on deep learning is proposed. In our model, stacked denoising autoencoders (SDAE), as encoder, automatically learns semantic encoding of heartbeats without any complex feature extraction in unsupervised way. Then bidirectional LSTM (Bi-LSTM) classifier achieves classification of heartbeats with semantic encoding. SDAE implements noise-reduction while Bi-LSTM takes full advantage of temporal information in data. At the same time, this method relieves impacts from unbalanced data by employing cost-sensitive loss function. We validate our model on MIT-BIH Arrhythmias Database, SVDB and NSTDB respectively. Compared with state-of-art methods, the final result verify that this newly proposed method not only has high accuracy but also boosts classifying efficiency.

Keywords

• Arrythmia
• bidirectional long short-term term network (Bi-LSTM)
• cost-sensitive learning
• denoise
• electrocardiogram (ECG)
• stacked denoising autoencoder (SDAE)