中国物理B ›› 2020, Vol. 29 ›› Issue (4): 40701-040701.doi: 10.1088/1674-1056/ab6d52

• SPECIAL TOPIC—Recent advances in thermoelectric materials and devices • 上一篇    下一篇

Preliminary abnormal electrocardiogram segment screening method for Holter data based on long short-term memory networks

Siying Chen(陈偲颖), Hongxing Liu(刘红星)   

  1. School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
  • 收稿日期:2019-12-23 修回日期:2020-01-13 出版日期:2020-04-05 发布日期:2020-04-05
  • 通讯作者: Siying Chen, Hongxing Liu E-mail:njhxliu@nju.edu.cn;siying_chen22@163.com,495777679@qq.com

Preliminary abnormal electrocardiogram segment screening method for Holter data based on long short-term memory networks

Siying Chen(陈偲颖), Hongxing Liu(刘红星)   

  1. School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
  • Received:2019-12-23 Revised:2020-01-13 Online:2020-04-05 Published:2020-04-05
  • Contact: Siying Chen, Hongxing Liu E-mail:njhxliu@nju.edu.cn;siying_chen22@163.com,495777679@qq.com

摘要: Holter usually monitors electrocardiogram (ECG) signals for more than 24 hours to capture short-lived cardiac abnormalities. In view of the large amount of Holter data and the fact that the normal part accounts for the majority, it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments, and then take the left segments to the doctors or the computer programs for further diagnosis. In this paper, we propose a preliminary abnormal segment screening method for Holter data. Based on long short-term memory (LSTM) networks, the prediction model is established and trained with the normal data of a monitored object. Then, on the basis of kernel density estimation, we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data. Based on these, the preliminary abnormal ECG segment screening analysis is carried out without R wave detection. Experiments on the MIT-BIH arrhythmia database show that, under the condition of ensuring that no abnormal point is missed, 53.89% of normal segments can be effectively obviated. This work can greatly reduce the workload of subsequent further processing.

关键词: electrocardiogram, long short-term memory network, kernel density estimation, MIT-BIH arrhythmia database

Abstract: Holter usually monitors electrocardiogram (ECG) signals for more than 24 hours to capture short-lived cardiac abnormalities. In view of the large amount of Holter data and the fact that the normal part accounts for the majority, it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments, and then take the left segments to the doctors or the computer programs for further diagnosis. In this paper, we propose a preliminary abnormal segment screening method for Holter data. Based on long short-term memory (LSTM) networks, the prediction model is established and trained with the normal data of a monitored object. Then, on the basis of kernel density estimation, we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data. Based on these, the preliminary abnormal ECG segment screening analysis is carried out without R wave detection. Experiments on the MIT-BIH arrhythmia database show that, under the condition of ensuring that no abnormal point is missed, 53.89% of normal segments can be effectively obviated. This work can greatly reduce the workload of subsequent further processing.

Key words: electrocardiogram, long short-term memory network, kernel density estimation, MIT-BIH arrhythmia database

中图分类号:  (Neural networks, fuzzy logic, artificial intelligence)

  • 07.05.Mh
02.50.Cw (Probability theory) 07.05.Kf (Data analysis: algorithms and implementation; data management)