An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset
Joint Authors
Wang, Zongmin
Gao, Junli
Zhang, Hongpo
Lu, Peng
Source
Journal of Healthcare Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-13
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems.
However, the complex variations and imbalance of ECG beats make this a challenging issue.
Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed.
For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples.
The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database.
Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals.
The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.
American Psychological Association (APA)
Gao, Junli& Zhang, Hongpo& Lu, Peng& Wang, Zongmin. 2019. An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1175288
Modern Language Association (MLA)
Gao, Junli…[et al.]. An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset. Journal of Healthcare Engineering No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1175288
American Medical Association (AMA)
Gao, Junli& Zhang, Hongpo& Lu, Peng& Wang, Zongmin. An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1175288
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175288