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.

中文翻译：

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一种利用MFCC特征进行睡眠阶段分类的自动方法

睡眠阶段分类是诊断睡眠障碍的必要步骤。通常，专家使用基于每30秒的生物信号的传统方法，例如眼电图（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，基于深度学习模型的各种最先进的方法已被证明在睡眠阶段分类方面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）与长短时记忆（LSTM）模型相结合的睡眠评分任务。从 EEG 和 EMG 信号中提取称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，然后将其输入到多个卷积层和LSTM层。其次，将学习到的表示馈送到全连接层和用于睡眠阶段分类的 softmax 分类器。这些实验在两个广泛使用的睡眠数据集——睡眠心脏健康研究（SHHS）和圣文森特大学医院/都柏林大学学院睡眠呼吸暂停（UCDDB）上进行，以测试该方法的有效性。这项研究的结果表明，该模型利用二维（2D）MFCC 特征的特征在睡眠阶段分类方面可以表现良好。使用该功能的优点是可以用来输入二维数据流，可以用来保留每个睡眠阶段的信息。使用 2D 数据流可以减少从一维流检索数据所需的时间。这种方法的另一个优点是它不需要深层，这有助于提高模型的性能。例如，通过减少层数，我们的七层模型结构需要大约 400 秒来训练和测试 SHHS1 数据集中的 100 个受试者。 SHHS 数据集的最佳准确率和 Cohen's kappa 分别为 82.35% 和 0.75，UCDDB 数据集的最佳准确率和 Cohen's kappa 分别为 73.07% 和 0.63。