KAN-SleepNet:一个结合科尔摩戈罗夫-阿诺德网络和双向LSTM的深度学习模型,用于使用EEG信号进行自动化睡眠分阶段
Zhenliang Xiong1,2,3, Yuxuan Gou4, Yinglin Zhou2,3
1Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.
Digital health
|November 21, 2025
概括
这项研究介绍了KAN-SleepNet,这是一种新的深度学习模型,用于使用电脑电图 (EEG) 信号自动测试睡眠阶段. KAN-SleepNet显著提高了睡眠阶段分类的准确性,为手动分析提供了更有效的替代方案.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
背景情况:
- 手动睡眠分阶段是耗时和劳动密集的,因此需要自动化解决方案.
- 准确的睡眠分期对于诊断睡眠障碍和评估睡眠质量至关重要.
- 单通道脑电图 (EEG) 信号为自动化睡眠分析提供了实际基础.
研究的目的:
- 提出KAN-SleepNet,一种用于自动化睡眠阶段分类的混合深度学习模型.
- 利用Kolmogorov-Arnold网络 (KAN) 和深度学习来增强从EEG信号中提取特征.
- 评估KAN-SleepNet与使用公共睡眠数据集的既定模型的有效性.
主要方法:
- 开发了KAN-SleepNet,集成了一个用于特征提取的ConvKAN块和用于时间依赖的双向LSTM.
- 在SleepEDF-78和ISRUC-S1数据集上训练和评估模型.
- 使用准确度,F1分数和科恩的卡帕评估性能,与SleepEEGNet和DeepSleepNet.Net等基线模型进行比较.
主要成果:
- 在这两组数据中,KAN-SleepNet的表现明显超过了大多数基线模型 (p < 0.05).
- 获得了高精度 (85.1%在SleepEDF-78,82.8%在ISRUC-S1) 和F1得分 (80.0%在SleepEDF-78,80.5%在ISRUC-S1).
- 在挑战性的N1睡眠阶段 (F1分数为53.2%和57.4%) 的分类中表现出强的表现.
结论:
- KAN-SleepNet为自动睡眠分阶段提供了卓越的性能.
- 该模型显示了作为临床睡眠分析的有效工具的潜力.
- 这种混合深度学习方法推进了自动化睡眠障碍诊断.
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