QRS错误分类对心率变化参数的影响 (CARLA队列研究的结果)
Frank Sauerbier1, Johannes Haerting1, Daniel Sedding2
1Institute of Medical Epidemiology, Biometrics, and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.
PloS one
|June 17, 2024
概括
模块化心电图分析系统 (MEANS) 可能会高估心率变化 (HRV) 由于错误分类的节拍,特别是在频率域参数中. 然而,这种自动化心电图分析工具适用于人口研究,因为它不会影响HRV与心血管疾病风险因素的关联.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 心率变化 (HRV) 使用心电图 (ECG) 数据量化自主神经系统活动.
- 像模块化心电图分析系统 (MEANS) 这样的自动化算法对于处理大型心电图数据集至关重要.
- 准确的节拍分类对于可靠的HRV分析至关重要.
研究的目的:
- 评估MEANS算法在检测非正弦节拍和文物方面的准确性.
- 将来自MEANS处理的HRV参数与临床医生验证的参考标准进行比较.
- 评估心电图处理对HRV和心血管疾病 (CVD) 风险因素之间的关联的影响.
主要方法:
- 分析了CARLA研究中1674名参与者的20分钟心电图记录.
- 使用MEANS算法处理的ECG数据,由专家临床医生建立的参考标准.
- 从5分钟段中计算HRV参数,并对HRV和心血管疾病风险因素关联的影响进行建模.
主要成果:
- MEANS实现了99%的整体节拍分类准确度,但对心室外脉的敏感性较低 (38%).
- 自动化处理导致了HRV参数的高估,特别是频域测量 (6.5-29%).
- 尽管HRV被高估,但HRV与心血管疾病风险因素之间的统计关联在所有处理方法中保持一致.
结论:
- 由于错误分类非正弦节拍,MEANS算法可能会高估HRV,特别是影响频率域参数.
- 在基于人群的研究中,这种过高估计对HRV和心血管疾病风险因素之间的关联的影响是可以忽略不计的.
- 使用MEANS进行自动化心电图分析是建议在大型人群研究中用于HRV的确定,因为其效率和风险因素关联中缺乏偏差.
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