Pei Wei, Li Yan, Wen Peng, Yang Fuwen, Ji Xiaopeng
School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
School of Engineering, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
Brain Inform. 2024 Feb 10;11(1):6. doi: 10.1186/s40708-024-00219-w.
Sleep stage classification is a necessary step for diagnosing sleep disorders. Generally, experts use traditional methods based on every 30 seconds (s) of the biological signals, such as electrooculograms (EOGs), electrocardiograms (ECGs), electromyograms (EMGs), and electroencephalograms (EEGs), to classify sleep stages. Recently, various state-of-the-art approaches based on a deep learning model have been demonstrated to have efficient and accurate outcomes in sleep stage classification. In this paper, a novel deep convolutional neural network (CNN) combined with a long short-time memory (LSTM) model is proposed for sleep scoring tasks. A key frequency domain feature named Mel-frequency Cepstral Coefficient (MFCC) is extracted from EEG and EMG signals. The proposed method can learn features from frequency domains on different bio-signal channels. It firstly extracts the MFCC features from multi-channel signals, and then inputs them to several convolutional layers and an LSTM layer. Secondly, the learned representations are fed to a fully connected layer and a softmax classifier for sleep stage classification. The experiments are conducted on two widely used sleep datasets, Sleep Heart Health Study (SHHS) and Vincent's University Hospital/University College Dublin Sleep Apnoea (UCDDB) to test the effectiveness of the method. The results of this study indicate that the model can perform well in the classification of sleep stages using the features of the 2-dimensional (2D) MFCC feature. The advantage of using the feature is that it can be used to input a two-dimensional data stream, which can be used to retain information about each sleep stage. Using 2D data streams can reduce the time it takes to retrieve the data from the one-dimensional stream. Another advantage of this method is that it eliminates the need for deep layers, which can help improve the performance of the model. For instance, by reducing the number of layers, our seven layers of the model structure takes around 400 s to train and test 100 subjects in the SHHS1 dataset. Its best accuracy and Cohen's kappa are 82.35% and 0.75 for the SHHS dataset, and 73.07% and 0.63 for the UCDDB dataset, respectively.
睡眠阶段分类是诊断睡眠障碍的必要步骤。一般来说，专家们使用基于生物信号每30秒的传统方法，如眼电图（EOG）、心电图（ECG）、肌电图（EMG）和脑电图（EEG）来对睡眠阶段进行分类。最近，各种基于深度学习模型的先进方法已被证明在睡眠阶段分类中具有高效且准确的结果。本文提出了一种新颖的深度卷积神经网络（CNN）与长短期记忆（LSTM）模型相结合的方法用于睡眠评分任务。从脑电图和肌电图信号中提取了一个名为梅尔频率倒谱系数（MFCC）的关键频域特征。所提出的方法可以从不同生物信号通道的频域中学习特征。它首先从多通道信号中提取MFCC特征，然后将其输入到几个卷积层和一个LSTM层。其次，将学习到的表示输入到一个全连接层和一个用于睡眠阶段分类的softmax分类器中。在两个广泛使用的睡眠数据集，即睡眠心脏健康研究（SHHS）和文森特大学医院/都柏林大学学院睡眠呼吸暂停（UCDDB）上进行实验，以测试该方法的有效性。本研究结果表明，该模型使用二维（2D）MFCC特征能够在睡眠阶段分类中表现良好。使用该特征的优点在于它可用于输入二维数据流，这可用于保留有关每个睡眠阶段的信息。使用二维数据流可以减少从一维流中检索数据所需的时间。该方法的另一个优点是它无需深层网络，这有助于提高模型的性能。例如，通过减少层数，我们模型结构的七层在SHHS1数据集中对100个受试者进行训练和测试大约需要400秒。对于SHHS数据集，其最佳准确率和科恩卡帕系数分别为82.35%和0.75，对于UCDDB数据集分别为73.07%和0.63。
