一个有效的系统来分类睡眠中的循环交替模式阶段
1ECE Department, Jaypee Institute of Information Technology, Noida, India.
Cognitive neurodynamics
|May 22, 2025
概括
这项研究提出了区分睡眠的新系统.
科学领域:
- 神经科学和生物医学工程
- 睡眠医学和信号处理
背景情况:
- 脑电图 (EEG) 信号对于睡眠分析至关重要.
- 睡眠期间EEG中的循环交替模式 (CAP) 对于诊断睡眠障碍至关重要.
- 准确区分CAP阶段 (A和B) 对于详细的睡眠分析至关重要.
研究的目的:
- 开发一个准确且易于实施的系统,用于区分CAP A阶段和EEG信号中的B阶段.
- 通过改进的CAP分析,提高睡眠障碍的早期诊断.
- 为了比较不同机器学习算法的性能,用于CAP阶段分类.
主要方法:
- 脑电图信号预处理:消噪和细分.
- 使用零相过,将信号分解为频率子频段.
- 使用Kruskal-Wallis测试进行统计特征提取和选择.
- 使用k-最近邻 (kNN),支向量机 (SVM),袋式树 (BT) 和神经网络 (NN) 算法进行分类.
主要成果:
- 袋式树 (BT) 分类器在组合均衡数据集上获得了最高的准确度 (83.29%) 和F-1得分 (83.58%).
- 与现有技术相比,拟议的方法显示出更高的准确性和效率.
- 数据集的成功分类包括健康受试者和失眠患者.
结论:
- 开发的系统提供了一个准确而有效的方法来区分EEG信号中的CAP相.
- 提出的方法有可能在睡眠障碍诊断中得到广泛的临床应用.
- 机器学习,特别是袋式树分类器,显示了自动化睡眠模式分析的前景.
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