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基于深度学习的ECG诊断的自适应波纹基选择:一种强化学习方法.

Qiao Xiao^1,2, Chaofeng Wang¹

¹School of Computer Science, University of South China, Hengyang, Hunan, China.

|February 3, 2025

概括

本研究引入了一种新的强化学习方法,用于在心电图 (ECG) 分析中选择波纹基. 这种方法动态优化特征提取,以改善心血管疾病诊断.

科学领域:

心脏病学心脏病学
信号处理信号处理
人工智能的人工智能

背景情况:

电心电图 (ECG) 信号对于诊断心血管疾病至关重要.
基于波形的特征提取与深度学习 (DL) 结合,显示出对心电图诊断的前景.
选择最佳的波段基是影响特征质量和诊断准确性的关键挑战.

研究的目的:

提出一种基于强化学习的波段基选择 (RLWBS) 框架,用于动态的,特定于信号的波段基定制.
解决ECG分析中传统的固定波量基的局限性.
通过优化特征提取来提高基于DL的ECG诊断的准确性.

主要方法:

开发了一个强化学习 (RL) 代理,以代优化波形基选择 (WBS) 策略.
RL代理人收到对分类业绩的反,以改进其WBS战略.
该框架可以动态定制各个ECG信号的波形基.

主要成果:

RLWBS框架实现了更详细的ECG信号的时间频率表示.
在PTB-XL数据集上的实验结果表明,对ECG异常分类的诊断性能有所提高.
提出的方法优于传统的WBS方法.

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结论:

使用强化学习的动态波基选择提供了比传统的心电图分析方法更优越的方法.
RLWBS框架可以显著提高深度学习模型在诊断心血管疾病方面的准确性.
这种适应性策略增强了从心电图信号中提取相关特征,从而获得更好的诊断结果.