IEEE J Biomed Health Inform. 2024 Nov;28(11):6641-6652. doi: 10.1109/JBHI.2024.3457969.
Sleep staging is a crucial task in sleep monitoring and diagnosis, but clinical sleep staging is both time-consuming and subjective. In this study, we proposed a novel deep learning algorithm named feature fusion temporal convolutional network (FFTCN) for automatic sleep staging using single-channel EEG data. This algorithm employed a one-dimensional convolutional neural network (1D-CNN) to extract temporal features from raw EEG, and a two-dimensional CNN (2D-CNN) to extract time-frequency features from spectrograms generated through continuous wavelet transform (CWT) at the epoch level. These features were subsequently fused and further fed into a temporal convolutional network (TCN) to classify sleep stages at the sequence level. Moreover, a two-step training strategy was used to enhance the model's performance on an imbalanced dataset. Our proposed method exhibits superior performance in the 5-class classification task for healthy subjects, as evaluated on the SHHS-1, Sleep-EDF-153, and ISRUC-S1 datasets. This work provided a straightforward and promising method for improving the accuracy of automatic sleep staging using only single-channel EEG, and the proposed method exhibited great potential for future applications in professional sleep monitoring, which could effectively alleviate the workload of sleep technicians.
睡眠分期是睡眠监测和诊断中的一项关键任务，但临床睡眠分期既耗时又主观。在这项研究中，我们提出了一种名为特征融合时间卷积网络（FFTCN）的新型深度学习算法，用于使用单通道 EEG 数据进行自动睡眠分期。该算法使用一维卷积神经网络（1D-CNN）从原始 EEG 中提取时间特征，使用二维卷积神经网络（2D-CNN）从通过连续小波变换（CWT）在epoch 级别生成的频谱图中提取时频特征。这些特征随后进行融合，并进一步输入到时间卷积网络（TCN）中，以在序列级别对睡眠阶段进行分类。此外，还使用两步训练策略来提高模型在不平衡数据集上的性能。我们提出的方法在健康受试者的 5 类分类任务中表现出优异的性能，在 SHHS-1、Sleep-EDF-153 和 ISRUC-S1 数据集上进行了评估。这项工作为仅使用单通道 EEG 提高自动睡眠分期的准确性提供了一种简单而有前途的方法，并且该方法在专业睡眠监测中的未来应用中具有很大的潜力，可以有效减轻睡眠技术员的工作量。
