基于LSTM和强化学习的电心电图特征波的划分
Mohammed Abdenacer Merbouti1, Dalila Cherifi2
1Université de Bejaia, Faculté de Technologie, Département de Génie Electrique, 06000, Bejaia, Algeria; University of Boumerdes, Institute of Electrical and Electronic Engineering, Laboratory Signals and Systems, Algeria.
Computers in biology and medicine
|September 13, 2025
概括
本研究引入了使用强化学习和LSTM进行心电图 (ECG) 特征波浪划分的新框架. 该方法提供了高效和精确的心电图分析,尽管在第三级心血管阻断中进行P波划分需要进一步研究.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 精确的心电图 (ECG) 特性波 (CW) 划分对于临床解释至关重要.
- 手动心电图分析是耗时的,而自动化方法面临的挑战是预细分和采样变化.
- 在三度心房静脉 (AV) 阻塞中,由于心房和心室节律分离,P波划分特别困难.
研究的目的:
- 开发一个强大的,高效的自动化框架,用于划分ECG信托点.
- 为了应对信号变异性和P波细分在复杂的心律失常症,如三度AV阻塞的挑战.
- 通过一种新的基于峰值的压缩方法来提高ECG分析的计算效率.
主要方法:
- 一个两阶段的框架,结合了基于强化学习 (RL) 的峰值分析仪和长短期记忆 (LSTM) 分类器.
- RL用于调整峰值分析器以选择突出的信号峰值,然后由LSTM将其分类为信托点 (P,QRS,T峰值和边界).
- 该框架旨在针对信号重新采样的稳定性,并结合了与节奏和形态相关的特征,以改进P波划分.
主要成果:
- 峰值分析仪实现了95.93%的非信息性样本减少,界限错误最小 (0-79 ms时间,0-0.079 mV振幅).
- 在各种数据集 (例如,CWD,QTDB,LUDB) 中报告了高灵敏度和积极预测值 (PPV) 的峰值和边界划分.
- 在高斯噪声下,性能呈现下降,特别是在较低的信号噪声比率 (5dB) 下.
结论:
- 拟议的框架允许精确的实时ECG划分,每次节拍运行时间为40.11毫秒,在时间误差方面表现优于相关工作.
- 虽然对于一般的心电图分析和信号压缩是有效的,但在第三级心血管阻断中P波划分仍然是未来研究的领域.
- 该方法表现出强大而高效的性能,为临床心电图分析应用提供了潜力.
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