通过将自适应式节拍细分和相对心率与深度学习网络相结合,进行心电图分类
Jaechan Lim1, Dong Han1, Mahdi Pirayesh Shirazi Nejad1
1Department of Biomedical Engineering, University of Connecticut, Storrs, 06269, CT, USA.
Computers in biology and medicine
|August 29, 2024
概括
本研究引入了用于心电图 (ECG) 分析的先进深度学习模型,改进了节拍分类和波形划分. 这种新的方法提高了检测心律失常的准确性,例如早收缩 (PAC).
科学领域:
- 心血管信号处理系统的信号处理.
- 医疗保健中的人工智能
- 用于医学诊断的深度学习.
背景情况:
- 准确的心电图 (ECG) 分析对于诊断心脏病状况至关重要.
- 传统方法在精确的波形划分和节拍类型分类方面面临挑战,特别是在微妙的节律失常方面.
- 现有的深度学习模型需要进一步优化,以进行复杂的心电图信号解释.
研究的目的:
- 开发一种最先进的深度学习方法,用于增强心电图信号分析.
- 为了提高心电图波形划分和节拍类型分类的准确性.
- 为了特别增强检测早收缩 (PACs) 和其他心律失常.
主要方法:
- 实施了一个深度学习模型,其中包含了两种新的节拍类型分类方案.
- 利用基于RR间隔的自适应节拍细分方法,以提高准确性.
- 整合相对心率信息,以区分类似的心跳,如PAC和正常心跳.
主要成果:
- 在节拍类型分类中实现了高灵敏度:正常节拍为99.81%,早发性心室收缩为99.08%,PAC为97.83%.
- 在波形划分方面获得了出色的F1分数:0.9842 (非波形),0.9798 (P波),0.9749 (QRS复合体) 和0.9848 (T波).
- 在多个基准数据集和现实数据中,与现有方法相比,表现优越,特别是在PAC检测方面.
结论:
- 建议的深度学习方法显著提高了ECG分析的准确性,无论是波形划分还是节拍分类.
- 集成的自适应细分和相对心率方案有效地改善了心律失常的检测,包括具有挑战性的PAC识别.
- 这种先进的方法提供了一个可靠的解决方案,用于使用心电图数据准确检测正常鼻节律和心律失常.
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