Automatic Sleep Stage Classification Method Research Articles

MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model

Background and ObjectiveSleep staging is an essential step for sleep disorder diagnosis, which is time-intensive and laborious for experts to perform this work manually. Automatic sleep stage classification methods not only alleviate experts from these demanding tasks but also enhance the accuracy and efficiency of the classification process. MethodsA novel multi-channel biosignal-based model constructed by the combination of a 3D convolutional operation and a graph convolutional operation is proposed for the automated sleep stages using various physiological signals. Both the 3D convolution and graph convolution can aggregate information from neighboring brain areas, which helps to learn intrinsic connections from the biosignals. Electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG) and electrocardiogram (ECG) signals are employed to extract time domain and frequency domain features. Subsequently, these signals are input to the 3D convolutional and graph convolutional branches, respectively. The 3D convolution branch can explore the correlations between multi-channel signals and multi-band waves in each channel in the time series, while the graph convolution branch can explore the connections between each channel and each frequency band. In this work, we have developed the proposed multi-channel convolution combined sleep stage classification model (MixSleepNet) using ISRUC datasets (Subgroup 3 and 50 random samples from Subgroup 1). ResultsBased on the first expert's label, our generated MixSleepNet yielded an accuracy, F1-score and Cohen kappa scores of 0.830, 0.821 and 0.782, respectively for ISRUC-S3. It obtained accuracy, F1-score and Cohen kappa scores of 0.812, 0.786, and 0.756, respectively for the ISRUC-S1 dataset. In accordance with the evaluations conducted by the second expert, the comprehensive accuracies, F1-scores, and Cohen kappa coefficients for the ISRUC-S3 and ISRUC-S1 datasets are determined to be 0.837, 0.820, 0.789, and 0.829, 0.791, 0.775, respectively. ConclusionThe results of the performance metrics by the proposed method are much better than those from all the compared models. Additional experiments were carried out on the ISRUC-S3 sub-dataset to evaluate the contributions of each module towards the classification performance.

Evaluating sleep-stage classification: how age and early-late sleep affects classification performance.

Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. Using wavelets for feature extraction and random forest for classification, an automatic sleep-stage classification method was sought and assessed. The age of the subjects, as well as the moment of sleep (early-night and late-night), were confronted to the performance of the classifier. From this study, we observed that these variables do affect the automatic model performance, improving the classification of some sleep stages and worsening others.

A CNN-BiLSTM Deep Learning Model for Automatic Scoring of EEG Signals

Abstract Recently, several automatic sleep stage classification methods have been proposed based on deep learning using convolutional (CNN) and recurrent (RNN) neural networks. However, the state of the art CNN methods are still complex which usually require significant time and considerable computational resources in order to set up and sufficiently train a deep CNN from scratch. This study eliminates the need to establish and train a deep CNN from scratch by leveraging a pre-trained deep architecture that has been previously trained from sufficient labeled data in a different context. A convolutional neural network (CNN) and a Bidrectional long short term memory network (BiLSTM) are integrated for automatic feature extraction and sleep stage scoring using only a singlechannel EEG signal. To demonstrate the generalizability of our results, the proposed model was evaluated using PSG records of 81 patients that were collected in different environments, through different recording hardware, and annotated with different sleep experts. The use of a single EEG source and a one-to-one classification scheme in the proposed model can allow further development towards wearable systems and online in home monitoring applications.

Cross-scenario automatic sleep stage classification using transfer learning and single-channel EEG

Sleep staging via manual inspection is time-consuming and inefficient, and as such, automatic sleep stage classification methods have been proposed and successfully implemented. However, these conventional supervised learning models are not suitable for cross-scenario sleep staging tasks due to the shift data distribution problem. This study aims to explore the use of transfer learning to transfer knowledge from a labeled source dataset to a new target domain where labels are difficult to obtain, so as to help solve the classification problem in a new domain in practical sleep staging scenarios. A novel end-to-end deep learning network that includes a feature extractor, a sleep stage classifier, and a domain adaptation network was proposed. The proposed domain adaptation network can accomplish distribution alignment between source and target domains, through the application of both joint distribution loss and stage transition loss. Cross-channel, cross-subject and channel, and cross-dataset experiments were designed to verify the feasibility of the proposed network using three publicly available datasets. The results indicate an average accuracy improvement ranging from 0.1% to 6.1% (average of 2.9%), a macro-averaging F1-score improvement ranging from 0.1% to 5.1% (average of 2.6%), and a Cohen’s kappa coefficient improvement ranging from 0.016 to 0.083 (average of 0.045) after using transfer learning, when compared with a non-transfer model. Relative to similar existing transfer learning methods for sleep staging, the proposed method was implemented in an unsupervised manner and achieved success in cross-scenario sleep staging tasks, which is more suitable for practical applications in daily life.

Comparison of Automatic Sleep Stage Classification Methods for Clinical Use

Sleep stage scoring is necessary for diagnosing several sleep disorders. However, it is an intensive and repetitive task and a vital automation candidate. This work seeks to evaluate different kinds of Machine Learning based classification algorithms available in the scientific literature to determine which one fits better the clinical practice requirements. The comparison is made with a predefined experimental design, using electroencephalography, electrooculography, and electromyography signals from the polysomnographic records of the Sleep-EDFx dataset. The comparison considers the accuracy and speed of algorithms based on Linear Discriminate Analysis, Support Vector Machines, Random Forests, and Artificial Neural Networks. The latter group includes the Deep Neural Networks DeapFeatureNet, based on Convolutional Neural Networks, and DeepSleepNet, additionally based on Recurrent Neural Networks. It is determined that several of the tested algorithms boast high accuracy levels (85%). From them, DeepSleepNet is chosen as the fittest due to its considerable advantage in execution time. Nevertheless, the final result should always be reviewed by the experts.

End-to-end Automatic Sleep Staging Algorithm using Convolution Neural Network and Bidirectional LSTM

In order to measure sleep quality, sleep experts manually classify sleep stages through polysomnography (PSG) signals. However, it is time-consuming and labor-intensive work. Thus, automatic sleep stage classification methods are needed. In this study, we propose an end-to-end automatic sleep staging algorithm using a one-dimensional convolutional neural network (1DCNN) based on an inception network and bidirectional long short-term memory (bi-LSTM). First, a feature map was extracted from input data using the 1D-CNN architecture without preprocessing. Secondly, bi-LSTM learned a stage transition rule using the feature maps. In addition, we used the sleep-EDF public dataset to evaluate our model, and only one channel of EEG signal was used to save computational cost. The accuracy and macro-averaged F1 score of the classification performance were 85.05% and 79.05%, respectively. These results demonstrate state-of-the-art performance compared to previous studies using the same dataset, yielding an effective method for an automatic sleep staging algorithm.

A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model

Sleep stage classification is an essential process for analyzing sleep and diagnosing sleep related disorders. Sleep staging by visual inspection of expert is a labor-intensive task and prone to subjective errors. In this paper, we proposed a single-channel EEG based automatic sleep stage classification model, called 1D-CNN-HMM. Our 1D-CNN-HMM combines deep one-dimensional convolutional neural network (1D-CNN) and hidden Markov model (HMM). We leveraged 1D-CNN for epoch-wise classification and HMM for subject-wise classification. The main idea of 1D-CNN-HMM model is to utilize the advantage of 1D-CNN that can automatically extract features from raw EEG, and HMM that can utilize sleep stage transition prior information of adjacent EEG epochs. To the best of author's knowledge, this is the first implementation of 1D-CNN connected with HMM in automatic sleep staging task. We used Sleep-EDFx dataset and DRM-SUB dataset, and performed subject-independent testing for model evaluation. Experimental results illustrated the overall accuracy and kappa coefficient of 1D-CNN-HMM could achieve 83.98% and 0.78 on Fpz-Oz channel EEG from Sleep-EDFx dataset, and achieve 81.68% and 0.74 on Cz-A1 channel EEG from DRM-SUB dataset. The overall accuracy and kappa coefficient of 1D-CNN-HMM outperformed other existing methods both on two datasets. In addition, the per-class performance of 1D-CNN-HMM is significantly higher than 1D-CNN on S1 and REM sleep stages with p<0.05. Our 1D-CNN-HMM outperformed other existing methods both on two datasets. Results also indicated that HMM improved the classification performance of 1D-CNN by improving the performance on S1 and REM stages.

Automatic Sleep Staging Based on EEG-EOG Signals for Depression Detection

In this paper, an automatic sleep scoring system based on electroencephalogram (EEG) and electrooculogram (EOG) signals was proposed for sleep stage classification and depression detection. Our automatic sleep stage classification method contained preprocessing based on independent component analysis, feature extraction including spectral features, spectral edge frequency features, absolute spectral power, statistical features, Hjorth features, maximum-minimum distance and energy features, and a modified ReliefF feature selection. Finally, a support vector machine was employed to classify four states (awake, light sleep [LS], slow-wave sleep [SWS] and rapid eye movement [REM]). The overall accuracy of the Sleep-EDF database reached 90.10 ± 2.68% with a kappa coefficient of 0.87 ± 0.04. Furthermore, a depression recognition method was developed to distinguish the patients with depression from healthy subjects. Specifically, according to the differences in sleep patterns between the two groups, REM latency, sleep latency, LS proportion, SWS proportion, sleep maintenance and arousal times were employed in this study. Sleep data from 12 healthy individuals and 19 patients with depression were applied to the system. The accuracy of the recognition results reached 95.24%, thus verifying the feasibility of our approach.

Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG

Background and objectiveIn recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learning hierarchical feature representation automatically from raw EEG data have been proposed. However, the state-of-the-art of such methods are quite complex. Using a simple CNN architecture to classify sleep stages is important for portable sleep devices. In addition, employing CNNs to learn rich and diverse representations remains a challenge. Therefore, we propose a novel CNN model for sleep stage classification. MethodsGenerally, EEG signals are better described in the frequency domain; thus, we convert EEG data to a time–frequency representation via Hilbert–Huang transform. To learn rich and effective feature representations, we propose an orthogonal convolutional neural network (OCNN). First, we construct an orthogonal initialization of weights. Second, to avoid destroying the orthogonality of the weights in the training process, orthogonality regularizations are proposed to maintain the orthogonality of weights. Simultaneously, a squeeze-and-excitation (SE) block is employed to perform feature recalibration across different channels. ResultsThe proposed method achieved a total classification accuracy of 88.4% and 87.6% on two public datasets, respectively. The classification performances of different convolutional neural networks models were compared to that of the proposed method. The experiment results demonstrated that the proposed method is effective for sleep stage classification. ConclusionsExperiment results indicate that the proposed OCNN can learn rich and diverse feature representations from time-frequency images of EEG data, which is important for deep learning. In addition, the proposed orthogonality regularization is simple and can be easily adapted to other architectures.

Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement

Sleep stage classification is a most important process in sleep scoring which is used to evaluate sleep quality and diagnose sleep-related diseases. Compared to complex sleep analysis devices, automatic sleep stage classification methods using single-channel electroencephalography (EEG) records benefit from the convenience of wearing and less interference in the sleep, thus are appropriate for home-based sleep analysis. In these methods, the design of representative features for classification plays the most important role in determining the performance. Previous works have not achieved satisfactory outcomes for ignoring several kinds of effective features. In this work, a novel multimodal signal decomposition and feature extraction strategy is presented to obtain effective features for sub-band signals. Meanwhile, a rule-free refinement process based on hidden Markov model (HMM) is proposed to optimize the classification results automatically. Experimental results show the superior classification performance of the proposed method compared to state-of-the-art works, wherein the rule-free refinement also outperforms previous rule-based correction algorithms. This sleep stage classification method is expected to contribute to the design of home-based sleep monitoring and analyzing system.

Automatic Sleep Stage Classification Based on Convolutional Neural Network and Fine-Grained Segments

Sleep stage classification plays an important role in the diagnosis of sleep-related diseases. However, traditional automatic sleep stage classification is quite challenging because of the complexity associated with the establishment of mathematical models and the extraction of handcrafted features. In addition, the rapid fluctuations between sleep stages often result in blurry feature extraction, which might lead to an inaccurate assessment of electroencephalography (EEG) sleep stages. Hence, we propose an automatic sleep stage classification method based on a convolutional neural network (CNN) combined with the fine-grained segment in multiscale entropy. First, we define every 30 seconds of the multichannel EEG signal as a segment. Then, we construct an input time series based on the fine-grained segments, which means that the posterior and current segments are reorganized as an input containing several segments and the size of the time series is decided based on the scale chosen depending on the fine-grained segments. Next, each segment in this series is individually put into the designed CNN and feature maps are obtained after two blocks of convolution and max-pooling as well as a full-connected operation. Finally, the results from the full-connected layer of each segment in the input time sequence are put into the softmax classifier together to get a single most likely sleep stage. On a public dataset called ISRUC-Sleep, the average accuracy of our proposed method is 92.2%. Moreover, it yields an accuracy of 90%, 86%, 93%, 97%, and 90% for stage W, stage N1, stage N2, stage N3, and stage REM, respectively. Comparative analysis of performance suggests that the proposed method is better, as opposed to that of several state-of-the-art ones. The sleep stage classification methods based on CNN and the fine-grained segments really improve a key step in the study of sleep disorders and expedite sleep research.

A two-step automatic sleep stage classification method with dubious range detection

BackgroundThe limitations of the current systems of automatic sleep stage classification (ASSC) are essentially related to the similarities between epochs from different sleep stages and the subjects׳ variability. Several studies have already identified the situations with the highest likelihood of misclassification in sleep scoring. Here, we took advantage of such information to develop an ASSC system based on knowledge of subjects׳ variability of some indicators that characterize sleep stages and on the American Academy of Sleep Medicine (AASM) rules. MethodsAn ASSC system consisting of a two-step classifier is proposed. In the first step, epochs are classified using support vector machines (SVMs) spread into different nodes of a decision tree. In the post-processing step, the epochs suspected of misclassification (dubious classification) are tagged, and a new classification is suggested. Identification and correction are based on the AASM rules, and on misclassifications most commonly found/reported in automatic sleep staging. Six electroencephalographic and two electrooculographic channels were used to classify wake, non-rapid eye movement (NREM) sleep – N1, N2 and N3, and rapid eye movement (REM) sleep. ResultsThe proposed system was tested in a dataset of 14 clinical polysomnographic records of subjects suspected of apnea disorders. Wake and REM epochs not falling in the dubious range, are classified with accuracy levels compatible with the requirements for clinical applications. The suggested correction assigned to the epochs that are tagged as dubious enhances the global results of all sleep stages. ConclusionsThis approach provides reliable sleep staging results for non-dubious epochs.