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

Journal: Computer methods and programs in biomedicine

PMID: 31200900

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.

Authors

Özal Yildirim

Computer Engineering Department, Engineering Faculty, Munzur University, Tunceli, Turkey. Electronic address: oyildirim@munzur.edu.tr.
Ulas Baran Baloglu

Department of Computer Engineering, Munzur University, Tunceli 62000, Turkey. baloglu@munzur.edu.tr.
Ru-San Tan

National Heart Centre Singapore, Singapore, Singapore.
Edward J Ciaccio

Department of Medicine, Celiac Disease Center, Columbia University, New York, USA.
U Rajendra Acharya

School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Darling Heights, Australia.

Keywords

Algorithms Arrhythmias, Cardiac Data Compression Data Management Databases, Factual Electrocardiography Electrocardiography, Ambulatory Humans Neural Networks, Computer Pattern Recognition, Automated Principal Component Analysis Reproducibility of Results Signal Processing, Computer-Assisted Software

External Resources

View on PubMed Access via DOI PubMed (31200900)

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

Abstract

Authors

Keywords

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