简单KANSleepNet:基于科尔莫戈罗夫-阿诺德网络的睡眠阶段分类方法
Xiaopeng Ji1, Lei Wang1, Yong Zhou1
1School of Computer Science and Technology/School of Artificial Intelligence, China University of Mining and Technology, Xuzhou, China.
Frontiers in bioinformatics
|March 6, 2026
概括
一个新的科尔摩戈罗夫-阿诺德网络 (KAN) 模型,简单KANSleepNet,改善了自动睡眠阶段的分类. 这种机器学习方法与使用多模式生物信号的现有方法相比,提供了具有竞争力的准确性和普遍性.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
背景情况:
- 自动睡眠阶段分类对于诊断睡眠障碍至关重要.
- 像卷积神经网络 (CNN) 和图形卷积网络 (GCN) 这样的现有方法在灵活性和性能上有局限性.
- 多层感知子 (MLP) 可以通过可学习的激活函数来增强,以改善建模.
研究的目的:
- 提出和评估一种基于Kolmogorov-Arnold网络 (KAN) 的新型模型,用于自动分类睡眠阶段.
- 用多个生物信号的时间和频率特征来评估SimpleKANSleepNet模型的性能.
- 为了比较KAN模型的有效性与已建立的CNN和GCN方法.
主要方法:
- 采用双流CNN架构,从EEG,EMG,EOG和ECG信号中提取时间和频率特征.
- 一个重新定义的多层感知器 (MLP) 架构使用Kolmogorov-Arnold网络 (KANs) 开发了可学习的激活功能.
- 在ISRUC-S1和Sleep-EDF-153数据集上评估了SimpleKANSleepNet模型.
主要成果:
- 简单KANSleepNet模型实现了高性能指标,包括整体准确性,F1得分和Cohen的kappa在两个数据集上.
- 对比分析显示,KAN模型的表现优于现有的基于CNN和GCN的方法.
- 研究了数据平衡技术和因素分析,以进一步优化分类性能.
结论:
- 拟议的基于KAN的SimpleKANSleepNet模型显示了用于准确和可概括的自动睡眠阶段分类的巨大潜力.
- 在生物医学信号处理的深度学习模型中,KANs为传统激活函数提供了灵活和有效的替代方案.
- 该研究强调了KANs在处理睡眠分析复杂生理数据方面的有效性.
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