从基于深度学习模型的动态心电图记录中检测心脏动
Yating Hu1, Tengfei Feng2, Miao Wang1
1School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.
Journal of personalized medicine
|May 27, 2023
概括
一种新的深度学习模型从心电图上准确检测心房动 (AF),识别其发病和偏移. 这一进步为这种常见的心律失常提供了更好的诊断,特别是在老年人群中.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 心房动 (AF) 是一种常见的临床心律失常,风险随着年龄的增长而增加.
- AF通常与冠状动脉疾病 (CAD) 和心力衰竭 (HF) 等并发症同时发生.
- 由于其间歇性和不可预测性,精确的AF检测具有挑战性.
研究的目的:
- 开发和验证一个深度学习模型,用于精确检测心房动.
- 为了从心电图 (ECG) 数据中识别AF发作和偏移.
- 提高诊断准确度,减少AF监控中的错误报警.
主要方法:
- 开发了一个包含残余块和变压器编码器的深度学习模型.
- 该模型使用来自CPSC2021挑战的动态心电图数据进行训练.
- 该方法没有区分心房动 (AF) 和心房动 (AFL).
主要成果:
- 该模型在AF节律测试中实现了高精度 (98.67%),灵敏度 (87.69%) 和特异性 (98.56%).
- 开始和偏移检测显示出高灵敏度 (分别为95.90%和87.70%).
- 0.46%的低虚假阳性率 (FPR) 将虚假警报降到最低. 噪音压力测试证实了强度.
结论:
- 深度学习模型有效地区分AF与正常心律.
- 该模型准确地检测到AF发作的开始和偏移.
- 特征可视化证实了模型的可解释性,重点关注关键的心电图波形特征.
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