通过使用智能手机音频录音在家庭环境中通过深度学习预测睡眠阶段:模型开发和验证
Hai Hong Tran1, Jung Kyung Hong1,2, Hyeryung Jang3
1Asleep Inc., Seoul, Republic of Korea.
Journal of medical Internet research
|June 1, 2023
概括
使用深度学习的基于声音的睡眠分期是可行的家庭使用,即使在背景噪音. 转移和一致性学习等先进的培训技术显著提高了准确性,使得基于智能手机的睡眠监测能够在没有特殊设备的情况下进行.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 睡眠科学 睡眠科学
背景情况:
- 公众对睡眠监测的兴趣正在增加,这推动了对家庭解决方案的需求.
- 使用深度学习的基于声音的睡眠分期是可穿戴设备的一个有希望的,方便的替代方案.
- 之前的研究仅使用实验室数据;家庭环境由于背景噪音而存在挑战.
研究的目的:
- 开发和验证一种深度学习方法,用于在不受控制的家庭环境中基于声音的睡眠阶段.
- 通过结合医院和家庭记录来解决缺乏注释的家庭睡眠数据的问题.
- 为了研究在家中仅使用智能手机音频用于睡眠分阶段的可行性.
主要方法:
- 一个深度学习模型被训练使用医院多睡眠学 (PSG) 和音频数据,增强家庭噪音.
- 转移学习结合了来自家庭环境的智能手机音频录音.
- 使用增强的医院声音数据来提高稳定性,应用了一致性培训.
主要成果:
- 该模型使用家庭录音实现了基于声音的睡眠分期准确率为76.2%.
- 在各种人口群体和睡眠呼吸暂停严重程度上,表现强.
- 将转移学习和一致性培训相结合,产生了最大的准确性改进 (+7.0%).
结论:
- 基于声音的睡眠分期是家庭使用的可行选择.
- 先进的深度学习技术使智能手机音频在现实环境中能够准确地预测睡眠阶段.
- 这种方法消除了对专用设备的需求,使睡眠监测更容易获得.
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