一个端到端的多通道卷积双LSTM网络,用于自动检测睡眠阶段
Tabassum Islam Toma1, Sunwoong Choi1
1School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种新的四通道卷积双向长期短期记忆 (Bi-LSTM) 网络,用于使用多睡眠学 (PSG) 数据自动检测睡眠阶段. 多通道方法通过有效利用EEG,EOG和EMG信号的时空特征来提高准确性.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 通过多睡眠学 (PSG) 自动检测睡眠阶段对于睡眠质量监测至关重要.
- 单道深度学习模型面临着数据低效和偏差的挑战.
- 多道方法提供了更好的性能,但需要大量的计算资源.
研究的目的:
- 引入一个计算效率高,多通道卷积双向长短期内存 (Bi-LSTM) 网络,用于自动检测睡眠阶段.
- 通过合并预先训练的双通道模块来提高性能来利用转移学习.
- 在睡眠阶段分类中解决模型性能和计算成本之间的权衡问题.
主要方法:
- 设计了一个四通道卷积Bi-LSTM网络,利用EEG Fpz-Cz,EEG Pz-Oz,EOG和EMG数据.
- 双通道卷积Bi-LSTM模块在PSG通道对上进行了预训练.
- 转移学习是通过合并两个预先训练的双通道模块来应用的.
- 一个双层卷积神经网络提取了空间特征,并与Bi-LSTM输入相合以捕获时间特征.
主要成果:
- 拟议的模型在使用EEG Fpz-Cz + EOG和EEG Fpz-Cz + EMG模块的睡眠EDF-20数据集 (ACC: 91.44%,Kp: 0.89,F1: 88.69%) 上实现了高精度.
- 在睡眠EDF-78数据集中,使用EEG Fpz-Cz + EMG和EEG Pz-Oz + EOG模块获得了最佳性能 (ACC: 90.21%,Kp: 0.86,F1: 87.02%).
- 一项比较分析表明,拟议模型的有效性与现有文献相比.
结论:
- 开发的多通道卷积Bi-LSTM网络有效地提取时空特征,用于准确的自动睡眠阶段检测.
- 使用融合双通道模块的转移学习方法提供了性能和计算效率之间的平衡.
- 该模型显示了改善睡眠监测系统的巨大潜力.
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