Arrhythmia classification of LSTM autoencoder based on time series anomaly detection

• Published in: Biomedical signal processing and control 2022-01, Vol.71, p.103228, Article 103228
• Main Authors: Liu, Pengfei, Sun, Xiaoming, Han, Yang, He, Zhishuai, Zhang, Weifeng, Wu, Chenxu
• Format: Article
• Language: English
• Subjects: Arrhythmia; Autoencoder; Deep learning; Heartbeat classification; LSTM
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.103228

•This method does not need to manually set the model input parameters.•This method avoids the problems of gradient disappearance and is more stable.•The model has simple structure and principle and high accuracy.•The effectiveness of this method is proved by comparative experiments. Electrocardiogram (ECG) is widely used in the diagnosis of heart disease because of its noninvasiveness and simplicity. The time series signals contained in the signal are usually obtained by the professional medical staff and used for the classification of heartbeat diagnosis. Professional physicians can use the electrocardiogram to know whether the patient has serious congenital heart disease and whether there is an abnormal heart structure. A lot of work has been done to achieve automatic classification of arrhythmia types. For example, Autoencoder can obtain the time series characteristics of ECG signals and be used for ECG signal classification. However, some traditional methods are abstruse and difficult to understand in principle. In the classification of arrhythmias carried out in recent years, some researchers only use Autoencoder to provide structural characteristics, without giving too much explanation to the design reasons. Therefore, we optimized a new network layer design based on LSTM to obtain the autoencoder structure. This structure can cooperate with the ECG preprocessing process designed by us to obtain better arrhythmia classification effect. This method enables direct input of ECG signals into the model without complicated preprocessing such as manual parameter input. Also, it eliminates the gradient vanishing problem existing in traditional convolutional neural network. We used five different types of ECG data in MIT-BIH arrhythmia database and MIT-BIH supraventricular arrhythmia database: atrial premature beats (APB), left bundle branch block (LBBB), normal heartbeat (NSR), right bundle branch block (RBBB) and ventricular premature beats (PVC). High accuracy, precision and recall were obtained. Compared with traditional methods, this method has better performance in arrhythmia classification.