一个多任务学习模型,使用RR间隔和呼吸力度来评估睡眠呼吸障碍
Jiali Xie1,2,3, Pedro Fonseca4,5,6, Johannes van Dijk4,7,8,6
1Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands. j.xie.1@tue.nl.
Biomedical engineering online
|May 5, 2024
概括
一种新的多任务模型使用心脏和呼吸系统信号准确估计睡眠呼吸障碍 (SDB) 的严重程度. 这种方法为SDB提供了可访问的查和随访,即使在睡眠效率低的患者中也是如此.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
- 医疗保健中的人工智能
背景情况:
- 睡眠呼吸障碍 (SDB) 需要可访问的诊断和监测工具.
- 目前的方法需要专门的设备,限制了广泛使用.
- 利用心脏和呼吸道信号为不引人注目的SDB评估提供了一个有希望的途径.
研究的目的:
- 引入一种新的多任务模型,用于同时进行睡眠-清醒分类和SDB事件检测.
- 为了自动估计SDB严重性评估的呼吸暂停-呼吸暂停指数 (AHI).
- 用随时可用的生理信号来评估模型的性能.
主要方法:
- 开发了一种结合卷积和循环神经网络的多任务模型.
- 该模型使用心电图和呼吸力信号的RR间隔进行训练.
- 对198名患者的多睡眠记录 (PSG) 进行了性能验证.
主要成果:
- 该模型在睡眠-清醒分类方面获得了0.70的Cohen's kappa,在总睡眠时间估计方面获得了0.830的Spearman's R.
- 估计与参考AHI的R值为0.891.
- 多任务模型的表现优于单任务模型,特别是在睡眠效率低的患者中 (R=0.861与R=0.746).
结论:
- 多任务模型自动准确地通过RR间隔和呼吸力度估计AHI和SDB的严重程度.
- 这项技术有可能通过不引人注目的传感器改进SDB查.
- 这种方法即使对睡眠效率较低的人来说也有效,而不需要额外的睡眠监测传感器.
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