循环交替模式睡眠阶段的基于深度学习的分类
Yoav Kahana1, Aviad Aberdam2, Alon Amar1
1Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, Technion-Israel Institute of Technology, Technion City, Haifa 3200003, Israel.
Entropy (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一种深度学习算法,用于使用脑电图 (EEG) 信号对睡眠中的循环交替模式 (CAP) 阶段进行分类. 这种新的方法实现了高精度,优于传统的机器学习方法,用于改善睡眠质量评估.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
背景情况:
- 睡眠中的周期交替模式 (CAP) 阶段对于睡眠质量评估至关重要.
- 目前的CAP分类方法主要使用经典机器学习,需要手动的特征提取.
- 深度学习方法用于CAP分类未得到充分利用.
研究的目的:
- 开发一个全自动的深度学习算法,用于对电脑电图 (EEG) 信号进行分类,用于CAP检测.
- 调查各种时间频率表示用于CAP分类的有效性.
- 通过利用上下文信息和专业数据增强来增强CAP识别.
主要方法:
- 使用卷积神经网络架构进行EEG信号分类.
- 用时间频率表示,特别是基于维格纳的方法,进行分析.
- 集成的上下文信息和数据增强,保持时间频率结构.
- 在公开可用的CAP睡眠数据库 (CAPSLPDB) 上训练模型.
主要成果:
- 基于维格纳的时间频率表示表现在CAP分类的短时间里埃变换上表现出优异的性能.
- 拟议的深度学习算法在平衡的测试集中达到77.5%的准确率,在不平衡的测试集中达到81.8%的准确率.
- 该算法在CAP分类准确性方面超过了现有的基于机器学习的方法.
结论:
- 开发的深度学习算法为自动CAP阶段识别提供了高效和可扩展的解决方案.
- 该方法适合在设备上实施,可能改善睡眠质量评估工具.
- 这项研究强调了深度学习和高级时间频率分析在睡眠研究中的潜力.
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