使用呼吸声在睡眠期间分离阻塞和中央呼吸道事件:利用深层卷积网络上的转移学习
Shumit Saha1, Nasim Montazeri Ghahjaverestan2, Azadeh Yadollahi3
1Department of Biomedical Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, USA; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
这项研究使用气管呼吸声音和深度学习来区分阻塞性睡眠呼吸暂停 (OSA) 和中央睡眠呼吸暂停 (CSA). 这种非侵入性方法为睡眠呼吸暂停提供了更容易获得的诊断工具.
科学领域:
- 生物医学工程 生物医学工程
- 呼吸系统医学 呼吸系统医学
- 医疗保健中的人工智能
背景情况:
- 多睡眠学 (PSG) 是睡眠呼吸暂停诊断的标准,但资源密集型.
- 当前的便携式设备往往无法区分阻塞性睡眠呼吸暂停 (OSA) 和中央睡眠呼吸暂停 (CSA).
- 由于其独特的病因和治疗方法,准确区分OSA和CSA至关重要.
研究的目的:
- 开发一种非侵入性,具有成本效益的方法来分类阻塞性和中央睡眠呼吸暂停事件.
- 为了利用气管呼吸声音和深 convolutional 神经网络 (CNNs) 进行呼吸暂停事件差异化.
- 为便携式诊断设备提供基础,能够区分OSA和CSA.
主要方法:
- 在6个预先训练的CNN (Alexnet,Resnet18,Resnet50,Densenet161,VGG16,VGG19) 上利用转移学习.
- 微调的CNN使用了50名参与者在PSG期间记录的气管道声音信号的谱图.
- 在数据集上训练并验证了模型,该数据集包含中央和阻塞性睡眠呼吸暂停事件.
主要成果:
- 在区分中心和阻塞性呼吸事件方面取得了高准确性.
- 综合CNN架构的整体准确率达到了83.66%.
- 报告的敏感度和特异性超过83%的事件分类.
结论:
- 气管呼吸声音有效地区分OSA和CSA.
- 这种方法为传统的PSG提供了一个不那么侵入性的,更容易获得的替代方案.
- 这些发现支持将这种技术应用于便携式设备,以加强睡眠呼吸暂停诊断和个性化治疗.
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