混合输入深度学习方法使用EEG信号对睡眠/清醒状态进行分类
1Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Diagnostics (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种混合深度学习模型,用于使用脑电图 (EEG) 信号进行自动睡眠-清醒分类. 这种新的方法实现了高精度,提高了睡眠分析的效率.
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
- 人工智能的人工智能
背景情况:
- 准确的睡眠阶段分类对于诊断睡眠障碍和相关健康状况至关重要.
- 手动睡眠分阶段是劳动密集型的,并且受得分者之间的变化影响.
- 自动睡眠分阶段的进步利用了多睡眠学数据,特别是电脑电图 (EEG) 信号.
研究的目的:
- 开发和评估一种混合深度学习模型,用于使用单通道EEG对睡眠和清醒状态进行分类.
- 提高自动睡眠阶段分类的准确性和效率.
主要方法:
- 开发了一种混合深度学习模型,将人工神经网络 (ANN) 和卷积神经网络 (CNN) 结合起来.
- 该ANN处理了来自EEG时代的统计特征.
- 美国有线电视新闻网分析了来自EEG时代的希尔伯特光谱图像.
- 该模型是从睡眠-EDF数据库的单通道Pz-OzEEG数据上进行训练和测试的.
主要成果:
- 拟议的混合模型在分类睡眠和清醒状态方面达到约96%的准确性.
- 在四个个人的EEG记录上评估了表现.
结论:
- 混合ANN-CNN模型在单通道EEG自动睡眠-清醒分类方面表现出高效率.
- 这种方法提供了一个有希望的,准确的,并可能更有效的替代手动睡眠分期.
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