Hasan Md Nazmul, Koo Insoo
Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Diagnostics (Basel). 2023 Jul 13;13(14):2358. doi: 10.3390/diagnostics13142358.
Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data. Manual sleep staging requires human expertise, which is occasionally prone to error and variation. In recent times, availability of polysomnography data has aided progress in automatic sleep-stage classification. In this paper, a hybrid deep learning model is proposed for classifying sleep and wake states based on a single-channel electroencephalogram (EEG) signal. The model combines an artificial neural network (ANN) and a convolutional neural network (CNN) trained using mixed-input features. The ANN makes use of statistical features calculated from EEG epochs, and the CNN operates on Hilbert spectrum images generated during each epoch. The proposed method is assessed using single-channel Pz-Oz EEG signals from the Sleep-EDF database Expanded. The classification performance on four randomly selected individuals shows that the proposed model can achieve accuracy of around 96% in classifying between sleep and wake states from EEG recordings.
睡眠阶段分类在从人类睡眠数据预测和诊断众多健康问题方面起着关键作用。人工睡眠分期需要专业知识，偶尔容易出现误差和差异。近年来，多导睡眠图数据的可用性推动了自动睡眠阶段分类的进展。本文提出了一种基于单通道脑电图（EEG）信号对睡眠和清醒状态进行分类的混合深度学习模型。该模型结合了使用混合输入特征训练的人工神经网络（ANN）和卷积神经网络（CNN）。ANN利用从EEG时段计算出的统计特征，而CNN对每个时段生成的希尔伯特频谱图像进行操作。使用来自扩展的睡眠-EDF数据库的单通道Pz-Oz EEG信号对所提出的方法进行评估。对四个随机选择个体的分类性能表明，所提出的模型在从EEG记录中区分睡眠和清醒状态时能够达到约96%的准确率。
