一个有效的混合特征选择使用重量方法用于自动睡眠分阶段
Weibo Wang1, Junwen Li1, Yu Fang1
1School of Electrical and Electronic Information, Xihua University, Chengdu 610039, People's Republic of China.
Physiological measurement
|October 2, 2023
概括
本研究介绍了一种混合特征选择方法,用于使用机器学习进行自动睡眠分阶段. 该方法有效地将185个特征减少到30个,在分类睡眠阶段方面实现了高精度.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 睡眠医学 睡眠医学
背景情况:
- 手动睡眠分期是主观的,耗时的.
- 使用机器学习的自动睡眠分阶段显示出希望,但受到高维,冗余特征的阻碍.
- 有效的特征选择对于提高自动睡眠分期精度至关重要.
研究的目的:
- 为自动睡眠分期提出混合功能选择方法.
- 为解决多重睡眠学 (PSG) 数据中冗余和无关的特征的问题.
- 为了提高睡眠阶段分类的准确性和效率.
主要方法:
- 四种模式的PSG信号 (EEG,EOG,ECG,EMG) 的预处理.
- 185个时间,频率和非线性特征的提取.
- 一个两阶段的混合特征选择:透重量方法与过方法相结合,其次是顺序向前选择.
- 使用SVM,KNN,随机森林和MLP组合分类模型.
主要成果:
- 混合方法选择了30个高度相关的特征,用于睡眠分阶段.
- 实现了88.86%的准确性,83.15%的F1得分和0.8531的卡帕系数,用于6级睡眠阶段.
- 与现有最先进的方法相比,表现出优越的性能.
结论:
- 建议的混合功能选择方法对自动睡眠分阶段是有效的.
- 显著减少特征维度,同时保持高分类性能.
- 为客观评估睡眠质量和诊断睡眠障碍提供了一个有前途的方法.
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