在CAPEEG序列上使用PSO优化的LSTM进行自动睡眠阶段分类
Manjur Kolhar1, Manahil Mohammed Alfuraydan1, Abdulaziz Alshammary1
1Department of Health Information Management and Technology, College of Applied Medical Sciences, King Faisal University, Al-Ahsa 36362, Saudi Arabia.
Brain sciences
|August 28, 2025
概括
这项研究引入了一个深度学习系统,用于从EEG数据中分类睡眠阶段和循环交替模式 (CAP) 亚型. 这种新的方法提高了睡眠分析的准确性和可解释性,
科学领域:
- 计算神经科学
- 医学中的人工智能
背景情况:
- 自动睡眠阶段和循环交替模式 (CAP) 亚型的EEG分类由于事件持续时间短和类不平衡而具有挑战性.
- 现有的方法难以处理睡眠微观结构的复杂性.
研究的目的:
- 开发一个特定领域的深度学习系统,以精确的基于EEG的睡眠阶段和CAP亚型分类.
- 使用混合优化技术增强模型性能和可解释性.
主要方法:
- 实施长期短期记忆 (LSTM) 网络.
- 使用粒子群优化-超频 (PSO-Hyperband) 混合超参数调方法进行优化.
- 应用基于SHAP的可解释性技术进行特征分析.
主要成果:
- 在CAP睡眠数据库中实现了97%的REM和96%的S0阶段准确性.
- 在挑战性CAP亚型 (A1-A3) 中获得的ROC AUC得分超过0.92.
- 通过确定主要的光谱和形态EEG特征,证明了模型透明度的提高.
结论:
- 拟议的框架有效地解决了阶级不平衡,并改善了相似的农业政策分类之间的歧视.
- 混合优化方法提高了睡眠微结构分析的深度学习模型的性能,通用性和可解释性.
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