通过深度音频细分来估计呼吸声的睡眠阶段分布
Seungeon Choi1, Joshep Shin1, Yunu Kim1
1Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究引入了一种基于音频的新框架,用呼吸声来估计睡眠阶段的分布. 该方法准确地预测睡眠阶段,为睡眠监测提供了多睡眠学的一种非侵入性替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠科学 睡眠科学
- 信号处理 信号处理
背景情况:
- 多睡眠学 (PSG) 是睡眠研究的黄金标准,但成本昂贵且侵入性.
- 常规和家庭睡眠监测需要非侵入性生物标志物.
- 呼吸动态与睡眠阶段相关,为被动监测提供了潜力.
研究的目的:
- 开发和验证一个框架来估计睡眠阶段分布 (清醒,轻度,深度,REM) 使用呼吸声.
- 为了利用呼吸速率和循环规律作为睡眠分期的非侵入性生物标志物.
- 为睡眠监测提供透明,无接触的方法.
主要方法:
- 一个基于变压器的架构是微调的,用于从音频中分割呼吸循环.
- 从细分的呼吸模式中提取了统计,光谱和分布特征.
- 用特定阶段的回归模型,包括TabPFN,来预测睡眠阶段的比例.
主要成果:
- 与基线方法相比,细分模块提高了预测呼吸速率和周期持续时间的准确性.
- 该框架实现了有利的根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 预测所有睡眠阶段的比例.
- TabPFN模型在睡眠阶段比例预测方面表现一致.
结论:
- 拟议的基于音频的框架有效地通过呼吸信号来估计睡眠阶段的分布.
- 这种方法提供了一种有希望的,非侵入性的,透明的睡眠监测方法.
- 该系统的可解释功能和避免黑子建模,增强了其临床支持的潜力.
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