深度学习模型用于预测单导电心电图的左心脏异常,用于可穿戴设备的开发
Masataka Sato1, Satoshi Kodera1, Naoto Setoguchi2
1Department of Cardiovascular Medicine, The University of Tokyo Hospital.
概括
新的心电图 (ECG) 模型可以从单个I心电图数据中检测左心脏异常. 这种可穿戴技术表现得与心脏病专家相提并论,改善了心力衰竭风险检测.
科学领域:
- 心脏病学 心脏病学
- 医疗技术 医疗技术 医学技术
- 人工智能的人工智能
背景情况:
- 左心脏异常会增加心力衰竭的风险.
- 心声回声仪并不总是可以获得的.
- 可穿戴式心电图 (ECG) 提供了早期检测的潜力.
研究的目的:
- 开发和验证用于检测左心脏异常的模型,使用单一的I心电图数据.
- 根据心脏病学家的解释,评估这些模型的性能.
主要方法:
- 开发了I型心电图模型,以识别低射出分数,墙壁运动异常,左心室缩和左心房/心室扩张.
- 用一个大型的多中心数据集 (229,439对联的心电图-心声图集) 进行培训和外部验证.
- 与12位心脏病学家解释12个心电图的模型性能进行了比较.
主要成果:
- 模型在接收器运行特征曲线下实现了高面积 (例如,低EF为0.913,LV扩张为0.838).
- 模型准确度达到78.3%的低射出分数和68.3%的左心室缩.
- 与心脏病学家相比,LVH的表现优越,低EF的表现相当于心脏病学家.
结论:
- I型心电图模型有效地预测了关键的左心脏异常.
- 这项技术展示了非侵入性,可访问的心脏评估的潜力.
- 这些模型的性能与专家心脏病学家的性能相当或优于专家心脏病学家.
相关概念视频
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An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
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Parts of an ECG
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