Automatic sleep stage classification: From classical machine learning methods to deep learning

The most important part of sleep quality assessment is the classification of sleep stages, which helps to diagnose sleep-related disease. In the traditional sleep staging method, subjects have to spend a night in the sleep clinic for recording polysomnogram. Sleep expert classifies the sleep stages by monitoring the signals, which is time consuming and frustrating task and can be affected by human error. New studies propose fully automated techniques for classifying sleep stages that makes sleep scoring possible at home. Despite comprehensive studies have been presented in this field the classification results have not yet reached the gold standard due to the concentration on the use of a limited source of information such as single channel EEG. Therefore, this article introduces a new method for fusing two sources of information, including electroencephalogram (EEG) and electrooculogram (EOG), to achieve promising results in the classification of sleep stages. In the proposed method, extracted features from the EEG and EOG signals, are divided into two feature sets consisting of the EEG features and fused features of EEG and EOG. Then, each feature set transformed into a horizontal visibility graph (HVG). The images of the HVG are produced in a novel framework and classified by proposed transfer learning convolutional neural network for data fusion (TLCNN-DF). Employing transfer learning at the training stage of the model has accelerated the training process of the CNN and improved the performance of the model. The proposed algorithm is used to classify the Sleep-EDF and Sleep-EDFx benchmark datasets. The algorithm can classify the Sleep-EDF dataset with an accuracy of 93.58% and Cohen’s kappa coefficient of 0.899. The results show proposed method can achieve superior performance compared to state-of-the-art studies on classification of sleep stages. Furthermore, it can attain reliable results as an alternative to conventional sleep staging.

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Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

Insomnia is a common sleep disorder in which patients cannot sleep properly. Accurate detection of insomnia disorder is a crucial step for disease analysis in the early stages. The disruption in getting quality sleep is one of the big sources of cardiovascular syndromes such as blood pressure and stroke. The traditional insomnia detection methods are time-consuming, cumbersome, and more expensive because they demand a long time from a trained neurophysiologist, and they are prone to human error, hence, the accuracy of diagnosis gets compromised. Therefore, the automatic insomnia diagnosis from the electrocardiogram (ECG) records is vital for timely detection and cure. In this paper, a novel hybrid approach based on the power spectral density (PSD) of the heart rate variability (HRV) is proposed to detect insomnia in three classification scenarios: (1) subject-based classification scenario (normal Vs. insomnia), (2) sleep stage-based classification (REM Vs. W. stage), and (3) the combined classification scenario using both subject-based and sleep stage-based features. The ensemble learning of random forest (RF) and decision tree (DT) classifiers are used to perform the first and second classification scenarios, while the linear discriminant analysis (LDA) classifier is used to perform the third combined scenario. The proposed framework includes data collection, investigation of the ECG signals, extraction of the signal HRV, estimation of the PSD, and AI-based classification via hybrid machine learning classifiers. The proposed framework is fine-tuned and evaluated using the free public Physio Net dataset over fivefold trails cross-validation. For the subject-based classification scenario, the detection performance in terms of sensitivity, specificity, and accuracy is recorded to be 96.0%, 94.0%, and 96.0%, respectively. For the sleep stage-based classification scenario, the detection evaluation results are recorded equally with 96.0% for ceiling level accuracy, sensitivity, and specificity. For the combined classification scenario, the LDA classifier have achieved the best insomnia detection accuracy of 99.0% of the three cases as discussed. In future, the proposed approach could be applicable for mobile observation schemes to automatically detect insomnia disorder. 

Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal

Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.

EEG-Based Sleep Staging Analysis with Functional Connectivity

Despite the progress recently made towards automatic sleep staging for adults, children have complicated sleep structures that require attention to the pediatric sleep staging. Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, greatly reduces the burden of epoch-by-epoch annotation for physicians. However, the inherent class-imbalance problem in sleep staging task undermines the effectiveness of semi-supervised methods such as pseudo-labeling. In this paper, we propose a Bi-Stream Adversarial Learning network (BiSALnet) to generate pseudo-labels with higher confidence for network optimization. Adversarial learning strategy is adopted in Student and Teacher branches of the two-stream networks. The similarity measurement function minimizes the divergence between the outputs of the Student and Teacher branches, and the discriminator continuously enhances its discriminative ability. In addition, we employ a powerful symmetric positive definite (SPD) manifold structure in the Student branch to capture the desired feature distribution properties. The joint discriminative power of convolutional features and nonlinear complex information aggregated by SPD matrices is combined by the attention feature fusion module to improve the sleep stage classification performance. The BiSALnet is tested on pediatric dataset collected from local hospital. Experimental results show that our method yields the overall classification accuracy of 0.80, kappa of 0.73 and F1-score of 0.76. We also examine the generality of our method on a well-known public dataset Sleep-EDF. Our BiSALnet exhibits noticeable performance with accuracy of 0.91, kappa of 0.85 and F1-score of 0.77. Remarkably, we have obtained comparable performance with state-of-the-art supervised approaches with fairly limited labeled data.

Adversarial learning for semi-supervised pediatric sleep staging with single-EEG channel.

Sleep stage classification, including wakefulness (W), rapid eye movement (REM), and non- rapid eye movement (NREM) which includes three sleep stages that describe the depth of sleep, is one of the most critical steps in effective diagnosis and treatment of sleep-related disorders. Clinically, sleep staging is performed by domain experts through visual inspection of polysomnography (PSG) recordings, which is time-consuming, labor-intensive and often subjective in nature. Therefore, this study develops an automatic sleep staging system, which uses single channel electroencephalogram (EEG) signal, for convenience of wearing and less interference in the sleep, to do automatic identification of various sleep stages. To achieve the automatic sleep staging system, this study proposes a two-layer stacked ensemble model, which combines the advantages of random forest (RF) and LightGBM (LGB), where RF focuses on reducing the variance of the proposed model while LGB focuses on reducing the bias of the proposed model. Particularly, the proposed model introduces a class balance strategy to improve the N1 stage recognition rate. In order to evaluate the performance of the proposed model, experiments are performed on two datasets, including Sleep-EDF database (SEDFDB) and Sleep-EDF Expanded database (SEDFEDB). In the SEDFDB, the overall accuracy (ACC), weight F1-score (WF1), Cohen’s Kappa coefficient (Kappa), sensitivity of N1 (SEN-N1) obtained by proposed model are 91.2%, 0.916, 0.864 and 72.52% respectively using subject-non-independent test (SNT). In parallel, the ACC, WF1, Kappa, SEN-N1 obtained by proposed model are 82.4%, 0.751, 0.719 and 27.15% respectively using subject-independent test (SIT). Experimental results show that the performance of the proposed model are competitive with the state-of-the-art methods and results, and the recognition rate of N1 stage is significantly improved. Moreover, in the SEDFEDB, the experimental results indicate the robustness and generality of the proposed model.

/pdf/automatic-sleep-stage-classification-with-single-channel-eeg-16mbaik2k0.pdf

Automatic Sleep Stage Classification With Single Channel EEG Signal Based on Two-Layer Stacked Ensemble Model

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Automatic Sleep Stage Classification With Single Channel EEG Signal Based on Two-Layer Stacked Ensemble Model