A new approach for arrhythmia classification using deep coded features and LSTM networks

Background and objective: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG)signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues. Methods: A convolutional auto-encoder (CAE)based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM)classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network. Results: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD)rate, and an accuracy of over 99.0% was observed. Conclusions: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.