改进的两层BiLSTM的心电信号分割方法

doi:10.12068/j.issn.1005-3026.2023.12.005

东北大学学报（自然科学版） ›› 2023, Vol. 44 ›› Issue (12): 1705-1711.DOI: 10.12068/j.issn.1005-3026.2023.12.005

改进的两层BiLSTM的心电信号分割方法

杨譞¹，何占奇²

(1.东北大学体育部，辽宁沈阳110819； 2.朝阳师范高等专科学校体育工作部，辽宁朝阳120000)

发布日期:2024-01-30
通讯作者: 杨譞
作者简介:杨譞(1984-)，女，黑龙江哈尔滨人，东北大学讲师.
基金资助:
-

Improved Two-layer BiLSTM Electrocardiosignal Segmentation Method

YANG Xuan¹， HE Zhan-qi²

1.Physical Education Department， Northeastern University， Shenyang 110819， China; 2.Physical Education Department， Chaoyang Teachers College， Chaoyang 120000， China.

Published:2024-01-30
Contact: YANG Xuan
About author:-
Supported by:
-

摘要/Abstract

摘要： 心电信号的分割方法可以有效地反映运动员的心脏功能和身体机能状况.通过人工对心电信号的手动分割往往耗费大量的时间和精力.为了实现自动化的心电信号分割，本文提出了一种改进的两层双向长短期记忆网络(bi-directional long short-term memory，BiLSTM)的心电图分割算法，可以前向和后向分析时间序列，以检测和定位重要波段，如P波、QRS波群和T波.实验使用公开QT数据集进行验证，以模拟运动员在赛前的心电数据.在与LSTM，BiLSTM以及两层BiLSTM的对比实验中，本方法的所有评价指标均有所提升.其准确率达95.68%，召回率为91.62%，精确度为91.05%，特异性为96.64%，F1分数为91.41%.结果表明该方法对心电信号进行分割具有较好的效果.

关键词: 长短期记忆；运动心电图；信号分割；循环神经网络；深度学习

Abstract: The segmentation method of ECG signals can effectively reflect athlete’s heart function and physical performance. Manual segmentation of ECG signals often consumes a lot of time and energy. In order to achieve automated ECG signal segmentation， this paper proposes improved two-layer BiLSTM electrocardiosignal segmentation algorithm， which can analyze time series forward and backward to detect and locate important waveforms such as the P-wave， QRS complex， and T-wave. The experiment used a publicly available QT database to simulate pre-competition ECG data of athletes. In comparative experiments with traditional LSTM， BiLSTM and two-layer BiLSTM， all evaluation indicators of this method have been improved. The average accuracy rate is 95.68%， the average recall rate is 91.62%， the average precision is 91.05%， the average specificity is 96.64%， and the average F1 score is 91.41%. The results showed that the proposed method has good performance in ECG signal segmentation.

Key words: long short-term memory; exercise electrocardiogram; signal segmentation; recurrent neural network; deep learning

中图分类号:

杨譞，何占奇. 改进的两层BiLSTM的心电信号分割方法[J]. 东北大学学报（自然科学版）, 2023, 44(12): 1705-1711.

YANG Xuan， HE Zhan-qi. Improved Two-layer BiLSTM Electrocardiosignal Segmentation Method[J]. Journal of Northeastern University(Natural Science), 2023, 44(12): 1705-1711.

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改进的两层BiLSTM的心电信号分割方法

Improved Two-layer BiLSTM Electrocardiosignal Segmentation Method

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