基于波纹的自动可解释的睡眠评分系统,用于疑似失眠,呼吸暂停和定期腿部运动的人群
Manisha Ingle1, Manish Sharma2, Shresth Verma2
1Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur-440010, Maharashtra, India.
Medical engineering & physics
|August 19, 2024
概括
这项研究引入了用于睡眠阶段分类的自动机器学习模型,在识别失眠和睡眠呼吸暂停等睡眠障碍方面实现了高准确性. 可解释的人工智能方法可以增强对这些条件的理解.
科学领域:
- 神经科学和生物医学工程
- 医疗保健中的人工智能
- 睡眠医学研究 睡眠医学研究
背景情况:
- 睡眠障碍影响着全球大量人口,需要通过睡眠阶段分类进行准确的诊断.
- 手动睡眠阶段分类从多睡眠学 (PSG) 是劳动密集型和主观.
- 自动化方法为简化睡眠障碍诊断和研究提供了一个有希望的解决方案.
研究的目的:
- 开发和评估用于睡眠阶段分类的自动机器学习模型.
- 将模型应用于具有和没有睡眠障碍 (失眠,PLM,睡眠呼吸暂停) 的大量数据集.
- 为了更好地理解分类模型,将可解释AI (XAI) 纳入.
主要方法:
- 利用了来自动脉样硬化多民族研究 (MESA) 队列 (NSRR) 的PSG数据.
- 采用基于波段的Hjorth参数,这些参数是从脑电图 (EEG) 的子频段中提取出来的,使用双直角波段过器银行 (BOWFB).
- 开发了一个集成袋式树木 (EnBT) 分类器,使用SHAP进行解释,使用三个EEG通道 (Fz_Cz, Cz_Oz, C4_M1).
主要成果:
- 自动化模型实现了高分类准确率:失眠86.8%,PLM87.3%,睡眠呼吸暂停85.0%,睡眠良好84.5%,完整数据集83.8%.
- SHAP方法为模型的决策过程提供了洞察力.
- 该模型有效地利用了所有道的特征进行分类.
结论:
- 使用基于波纹的Hjorth参数和EnBT分类器的拟议自动化技术显著提高了睡眠阶段分类的准确性.
- XAI的功能提高了模型的解释性,有助于理解睡眠障碍.
- 这种方法有可能改善失眠,PLM和睡眠呼吸暂停的诊断和管理.
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