使用呼吸力度提高了基于心电图的深度学习算法,以评估睡眠呼吸障碍的呼吸
Jiali Xie1, Pedro Fonseca1,2, Johannes P van Dijk1,3,4
1Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
从心电图 (ECG) 获得的呼吸努力,与ECG衍生的呼吸 (EDR) 相比,显著改善睡眠呼吸暂停检测和严重性评估. 这证实了算法.
科学领域:
- 心脏病学 心脏病学
- 睡眠医学 睡眠医学
- 医疗技术 医疗技术 医学技术
背景情况:
- 睡眠呼吸暂停 (Sleep Apnea) 是一种常见的睡眠呼吸障碍,影响全球数以百万计的人.
- 电心电图 (ECG) 信号,包括心率和ECG衍生的呼吸 (EDR),显示出睡眠呼吸暂停检测的潜力.
- 在睡眠呼吸暂停中,EDR可能不是最佳的呼吸评估措施.
研究的目的:
- 评估基于心电图的深度学习算法用于睡眠呼吸暂停检测.
- 在睡眠呼吸暂停检测中,比较胸腔呼吸努力与EDR的性能.
- 评估算法在估计呼吸暂停-呼吸暂停指数 (AHI) 和分类睡眠暂停严重程度方面的准确性.
主要方法:
- 使用198名患者的独立数据集来评估基于心电图的深度学习算法.
- 用胸腔呼吸力与EDR用于睡眠呼吸暂停事件检测的算法性能进行了比较.
- 此外,还评估了呼吸暂停-呼吸暂停指数 (AHI) 估计和睡眠暂停严重程度分类.
主要成果:
- 使用呼吸力而不是EDR改善了睡眠呼吸暂停事件的检测 (F1分数 = 0.708).
- 呼吸力增加了AHI估计的准确性 (斯皮尔曼相关性=0.922).
- 基于AHI的睡眠呼吸暂停严重程度分类显示出良好的表现 (科恩卡帕=0.62).
结论:
- 与EDR相比,胸腔呼吸力是评估睡眠呼吸暂停的优越方法.
- 基于心电图的算法在独立数据集中表现良好,验证了它对睡眠呼吸暂停评估的有用性.
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