A new approach for arrhythmia classification using deep coded features and LSTM networks. (July 2019)

• Record Type: Journal Article
• Authors: Yildirim, Ozal; Baloglu, Ulas Baran; Tan, Ru-San; Ciaccio, Edward J.; Acharya, U. Rajendra
• Is Part Of: Computer methods and programs in biomedicine. Volume 176(2019)
• Page Start: 121
• Page End: 133
• Publication Date: 2019-07
• Subjects: Arrhythmia detection -- ECG compression -- Deep learning -- Autoencoders -- LSTM; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.05.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: High performance classification of arrhythmia types with LSTM and CAE-LSTM based structures. Signal sizes of arrhythmic beats are reduced by convolutional autoencoders. ECG signals were compressed by an average 0.70% PRD rate, and an accuracy of over 99.0%. Significant improvement in time cost of LSTM networks for ECG data analysis. Abstract: 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.