SiCRNN:通过气管道麦克风信号识别睡眠呼吸暂停的语方法
Davide Lillini1, Carlo Aironi1, Lucia Migliorelli1
1Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
使用气管麦克风的新型深度学习系统可以检测睡眠呼吸暂停 (SAS) 事件,其回忆率为95%. 这种方法为识别这种常见的睡眠障碍提供了更容易使用的方法.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 睡眠医学 睡眠医学
背景情况:
- 睡眠呼吸暂停综合征 (SAS) 影响全球数百万人,但经常未被诊断出来.
- 目前通过多睡眠图 (PSG) 进行诊断是侵入性的,并依赖于主观的临床医生的解释.
- 需要客观,可访问的SAS检测方法.
研究的目的:
- 开发和评估一种新的决策支持系统,用于检测睡眠呼吸暂停事件.
- 使用气管麦克风和深度学习模型进行客观SAS检测.
- 为了克服传统的多睡眠术的局限性.
主要方法:
- 开发了一个深度学习模型,SiCRNN (罗卷积循环神经网络).
- 该系统处理了来自气管麦克风录音的Mel光谱图.
- 无呼吸事件的检测是使用k-means集群完成的.
主要成果:
- SiCRNN模型在呼吸暂停事件中实现了高达95%的回忆得分.
- 实验运行优化了网络配置和数据参数.
- 与完全卷积基线相比,罗式训练范式表现出优异的性能.
结论:
- 拟议的气管麦克风和SiCRNN系统为睡眠呼吸暂停检测提供了一种有效的方法.
- 这种方法有望提高SAS诊断的可访问性和客观性.
- 语深度学习模式增强了对睡眠呼吸暂停事件的识别.
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