来自智能设备的不确定的单线心电图:基线心电图异常的影响
Corinne Isenegger1, Diego Mannhart1, Simon Weidlich1
1Department of Cardiology, University Hospital Basel, Basel, Switzerland; Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland.
基线心电图 (ECG) 异常显著影响智能设备节律分类的准确性. 预先存在的条件,如文物或节奏,增加了可穿戴心电图设备不确定的读数的可能性.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数字健康数字健康
背景情况:
- 智能设备提供单导电心电图 (SL-ECG) 记录与自动节律分类.
- 预先存在的心电图异常对自动分类准确性的影响尚不清楚.
研究的目的:
- 评估预定义的心电图异常对五种商业智能设备节奏分类准确性的影响.
- 在存在心电图异常的情况下,比较FDA和CE标记的可穿戴智能设备的性能.
主要方法:
- 一项前性研究,涉及256名接受电生理学手术的患者.
- 使用五个智能设备 (AliveCor KardiaMobile,Apple Watch 6,Fitbit Sense,Samsung Galaxy Watch 3,Withings ScanWatch) 记录了12导电心电图和SL-ECG. 这是一个非常简单的过程.
- 由两名心脏病学家进行手动节奏分类和对心电图异常 (节奏,导电延迟,低电压,人工物,过早复合) 的评估.
主要成果:
- 分析了1,280个SL-ECG;19%被至少一个设备归类为不确定的.
- 不确定的SL-ECG具有更高的ECG异常流行率 (74%对42%;P<0.001).
- 主要文物 (OR 10.62) 和心室节奏 (OR 6.35) 显著增加了不确定的分类的几率,具有显著的设备特异性差异.
结论:
- 智能设备的自动节律分类对基线心电图异常非常敏感.
- 根据个体特征,研究结果指导选择合适的患者和智能设备进行心律失常监测.
- 强调在使用可穿戴心律失常检测器时,需要仔细考虑心电图质量和患者病史.
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