多模式多任务深度神经网络用于使用心脏和音频信号评估睡眠呼吸障碍
Jiali Xie1, Pedro Fonseca2, Johannes P van Dijk3
1Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5612 AP, the Netherlands.
International journal of medical informatics
|April 26, 2025
概括
将心脏和音频信号与深度学习相结合,可以改善睡眠障碍的呼吸检测. 这种多模式的方法为诊断睡眠呼吸暂停提供了比传统的多睡眠学更容易获得和更具成本效益的替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
- 人工智能的人工智能
背景情况:
- 睡眠呼吸障碍 (SDB) 是一种普遍存在的睡眠障碍,对健康有重大影响,包括白天功能障碍和心血管疾病风险.
- 目前的诊断方法,如多睡眠学是昂贵和难以获得的,需要开发负担得起和用户友好的检测技术.
- 心脏和音频信号为家庭 SDB 监控提供了不引人注目的传感选项.
研究的目的:
- 开发和评估用于SDB评估的多模式深度学习模型.
- 利用心脏和音频信号的互补信息来改善SDB检测和严重程度分类.
- 为了估计呼吸暂停-呼吸暂停指数 (AHI),并对SDB严重程度进行分类.
主要方法:
- 采用了一种多模式,多任务的深度学习框架.
- 输入特征包括心跳间隔,心电图衍生的呼吸,以及来自音频录音的Mel尺度频率切普斯特尔系数.
- 模型使用这些心脏和音频衍生的特征的各种组合进行训练.
主要成果:
- 该研究在SDB事件检测方面取得了0.588的F1得分.
- 获得0.825的相关系数,以估计呼吸暂停-呼吸暂停指数.
- SDB严重程度分类的准确性达到57.8%的正常,轻度,中度和严重的类别.
结论:
- 结合心脏和音频信号显著提高SDB检测性能.
- 多式联络数据融合显示,有望推动研究可访问的睡眠呼吸暂停诊断.
- 这种方法为实现更广泛和更方便的SDB选提供了潜在的途径.
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