AF Detection by Exploiting the Spectral and Temporal Characteristics of ECG Signals with the LSTM Model

Yen Chun Chang, Sau-Hsuan Wu*, Li Ming Tseng, Hsi-Lu Chao, Chun Hsien Ko

*此作品的通信作者

研究成果: Conference contribution同行評審

22 引文 斯高帕斯(Scopus)

摘要

This research reinvestigates the detection of atrial fibrillation (AF) from a recurrent neural network (RNN) viewpoint. In particular, a long short-term memory (LSTM) model of RNN is designed to exploit the high-order spectral and temporal features of the multi-lead electrocardiogram (ECG) signals of patients with AF. To verify the proposed method, the LSTM model is tested with ECG data available from the PhysioNet and some normal ECG data collected in our labs. The results show that not only the deviation of the so-called RR intervals of ECG signals but also its temporal variations are critical to AF detection. The accuracy of AF detection can reach up to 98.3 %, with an LSTM model of using 30 hidden units. Considering more realistic applications, we further tested the model with subjects different from that of the training data. The accuracy is about 87% with high sensitivity. The experimental results show that the proposed model is able to effectively extract both the long-term and short-term characteristics of the spectral content of the AF ECG signals, making it a good candidate model for AF detection.

原文American English
主出版物標題Computing in Cardiology Conference, CinC 2018
發行者IEEE Computer Society
ISBN(電子)9781728109589
DOIs
出版狀態Published - 1 9月 2018
事件45th Computing in Cardiology Conference, CinC 2018 - Maastricht, Netherlands
持續時間: 23 9月 201826 9月 2018

出版系列

名字Computing in Cardiology
2018-September
ISSN(列印)2325-8861
ISSN(電子)2325-887X

Conference

Conference45th Computing in Cardiology Conference, CinC 2018
國家/地區Netherlands
城市Maastricht
期間23/09/1826/09/18

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