Automatic Sleep Stage Classification Research Articles

An intelligent Model for EEG Sleep Stages Classification Using Wavelet Transform Based Hybrid Features extraction Model

Background: High-quality sleep plays a major role in improving brain development and lifestyle. Electroencephalogram (EEG) signals are the most important signals collected by polysomnography (PSG) used for sleep staging. Manual sleep scoring is a difficult task, however, developing an automatic sleep stage is important to support experts to detect sleep disorders in early stage. Method: In this paper, an automatic single-channel EEG signal sleep stages classification model is proposed. A Discrete Wavelet Transform (DWT) based EEG feature is suggested. Three types of features including entropy, linear, and statistical features are extracted and evaluated to score sleep stages. First, we applied the DWT to each 30-second epoch to decompose the signal into five bands. Then, EEG features are extracted from each band. EEG signals from two datasets named Dreams, and EFD sleep are used to evaluate the proposed model. Results: We interpreted the results using essential statistical criteria. The results showed that the use of combination features improves the sleep classification results. Based on the results, with the Dream dataset, the classification accuracy rate, Kappa coefficient, and F-score were found 0.97,0.92, 0.95 For the second database, we obtained 0.95,0.94, 0.93 for accuracy rate, Kappa coefficient and F-score respectively. Conclusions: We developed a method to score sleep stages that can be used by healthcare providers to identify sleep disorders.Keywords: DWT,

MultiSEss: Automatic Sleep Staging Model Based on SE Attention Mechanism and State Space Model.

Sleep occupies about one-third of human life and is crucial for health, but traditional sleep staging relies on experts manually performing polysomnography (PSG), a process that is time-consuming, labor-intensive, and susceptible to subjective differences between evaluators. With the development of deep learning technologies, particularly the application of convolutional neural networks and recurrent neural networks, significant progress has been made in automatic sleep staging. However, existing methods still face challenges in feature extraction and cross-modal data fusion. This paper introduces an innovative deep learning architecture, MultiSEss, aimed at solving key issues in automatic sleep stage classification. The MultiSEss architecture utilizes a multi-scale convolution module to capture signal features from different frequency bands and incorporates a Squeeze-and-Excitation attention mechanism to enhance the learning of channel feature weights. Furthermore, the architecture discards complex attention mechanisms or encoder-decoder structures in favor of a state-space sequence coupling module, which more accurately captures and integrates correlations between multi-modal data. Experiments show that MultiSEss achieved accuracy results of 83.84% and 82.30% in five-fold cross-subject testing on the Sleep-EDF-20 and Sleep-EDF-78 datasets. MultiSEss demonstrates its potential in improving sleep stage accuracy, which is significant for enhancing the diagnosis and treatment of sleep disorders.

FlexibleSleepNet:A Model for Automatic Sleep Stage Classification Based on Multi-Channel Polysomnography.

In the task of automatic sleep stage classification, deep learning models often face the challenge of balancing temporal-spatial feature extraction with computational complexity. To address this issue, this study introduces FlexibleSleepNet, a lightweight convolutional neural network architecture designed around the Adaptive Feature Extraction (AFE) Module and Scale-Varying Compression (SVC) Module. Through multi-channel polysomnography data input and preprocessing, FlexibleSleepNet utilizes the AFE Module to capture intra-channel features and employs the SVC Module for channel feature compression and dimension expansion. The collaborative work of these modules enables the network to effectively capture temporal-spatial dependencies between channels. Additionally, the network extracts feature maps through four distinct stages, each from different receptive field scales, culminating in precise sleep stage classification via a classification module. This study conducted k-fold cross-validation on three different databases: SleepEDF-20, SleepEDF-78, and SHHS. Experimental results show that FlexibleSleepNet demonstrates superior classification performance, achieving classification accuracies of 86.9% and 87.6% on the SleepEDF-20 and SHHS datasets, respectively. It performs particularly well on the SleepEDF-78 dataset, where it reaches a classification accuracy of 87.0%. Additionally, it has significantly enhanced computational efficiency while maintaining low computational complexity.

Towards interpretable sleep stage classification with a multi-stream fusion network

Sleep stage classification is a significant measure in assessing sleep quality and diagnosing sleep disorders. Many researchers have investigated automatic sleep stage classification methods and achieved promising results. However, these methods ignored the heterogeneous information fusion of the spatial–temporal and spectral–temporal features among multiple-channel sleep monitoring signals. In this study, we propose an interpretable multi-stream fusion network, named MSF-SleepNet, for sleep stage classification. Specifically, we employ Chebyshev graph convolution and temporal convolution to obtain the spatial–temporal features from body-topological information of sleep monitoring signals. Meanwhile, we utilize a short time Fourier transform and gated recurrent unit to learn the spectral–temporal features from sleep monitoring signals. After fusing the spatial–temporal and spectral–temporal features, we use a contrastive learning scheme to enhance the differences in feature patterns of sleep monitoring signals across various sleep stages. Finally, LIME is employed to improve the interpretability of MSF-SleepNet. Experimental results on ISRUC-S1 and ISRUC-S3 datasets show that MSF-SleepNet achieves competitive results and is superior to its state-of-the-art counterparts on most of performance metrics.

Probability estimation of narcolepsy type 1 in DTA mice using unlabeled EEG and EMG data.

The manual evaluation of mouse sleep studies is labor-intensive and time-consuming. Although several approaches for automatic sleep stage classification have been proposed, no automatic pipeline for detecting a specific mouse phenotype has yet been developed. Here, we present a fully automated pipeline for estimating the probability of Narcolepsy Type 1 (NT1) in the hypocretin-tTA;TetO-Diphteria toxin A (DTA) mouse model using unlabeled electroencephalographic (EEG) and electromyographic (EMG) data. The pipeline is divided into three modules: (1) automatic sleep stage classification, (2) feature extraction, and (3) phenotype classification. We trained two automatic sleep stage classifiers, UsleepEEG and UsleepEMG, using data from 83 wild-type (WT) mice. We next computed features such as EEG spectral power bands, EMG root mean square, and bout metrics from 11 WT and 19 DTA mice. The features were used to train an L1-penalized logistic regression classifier in a Leave-One-Subject-Out approach, achieving an accuracy of 97%. Finally, we validated the pipeline in a held-out dataset of EEG/EMG recordings at four different timepoints during disease development in seven DTA mice, finding that the pipeline captured disease progression in all mice. While our pipeline generalizes well to data from other laboratories, it is sensitive to artifacts, which should be considered in its application. With this study, we present a pipeline that facilitates a fast assessment of NT1 probability in the DTA model and thus can accelerate large-scale evaluations of NT1 treatments.

Hypnogram and Hypnodensity Analysis of REM Sleep Behaviour Disorder Using Both EEG and HRV-Based Sleep Staging Models.

Rapid-eye-movement (REM) sleep behaviour disorder (RBD) is a primary sleep disorder strongly associated with Parkinson's disease. Assessing sleep structure in RBD is important for understanding the underlying pathophysiology and developing diagnostic methods. However, the performance of automated sleep stage classification (ASSC) models is considered suboptimal in RBD, for both models utilising neurological signals ("ExG": EEG, EOG, and chin EMG) and heart rate variability combined with body movements (HRVm). Here, we explore this underperformance through the categorical representation of sleep macrostructure (i.e., hypnogram) and a representation that leverages the underlying probability distribution of ASSCs (i.e., hypnodensity). By comparing the RBD population (n = 36) to a sex- and age-matched group of OSA patients chosen for their anticipated similarly decreased sleep stability, we confirm lower 4-stage classification performance in both ExG-based ASSC (RBD: κ = 0.74, OSA: κ = 0.80) and HRVm-based ASSC (RBD: κ = 0.50, OSA: κ = 0.63). Stages showing lower agreement in RBD, namely, N1 + N2 and REM sleep, exhibited elevated ambiguity in the hypnodensity, indicating more ambiguous classification distributions. Limited differences in bout durations between RBD and OSA suggested sleep instability is not necessarily driving lower agreement in RBD. However, stage transitions in OSA showed more abrupt changes in the underlying probability distribution, while RBD transitions had a more continuous profile, possibly complicating classification. Although both ExG-based and HRVm-based automated sleep staging in RBD remain challenging, hypnodensity analysis is informative for the characterisation of (RBD) sleep and can capture potential drivers of classification disagreement.

A review of automatic sleep stage classification using machine learning algorithms based on heart rate variability.

Over the past few decades, the use of heart rate variability (HRV) has expanded significantly due to its ease of collection, affordability, and its clinical relevance to psychophysiological processes and psychopathological disorders. This study aims to demonstrate the effectiveness of an artificial intelligence approach based on HRV signals for automatic sleep stage classification. This review examines machine learning algorithms for HRV-based sleep stage classification over the past 15years. It also compares the HRV features extracted, the classification algorithms used, and the evaluation parameters employed. Existing studies indicate that with advances in technology, machine learning algorithms utilizing HRV features for sleep staging achieve high accuracy, sensitivity, and specificity. The use of HRV for sleep analysis via machine learning algorithms is an active area of research with broad application potential. As technology progresses and data accumulation increases, this approach is expected to offer more accurate and personalized solutions for sleep medicine and health management.

Multimodal sleep staging network based on obstructive sleep apnea.

Automatic sleep staging is essential for assessing sleep quality and diagnosing sleep disorders. While previous research has achieved high classification performance, most current sleep staging networks have only been validated in healthy populations, ignoring the impact of Obstructive Sleep Apnea (OSA) on sleep stage classification. In addition, it remains challenging to effectively improve the fine-grained detection of polysomnography (PSG) and capture multi-scale transitions between sleep stages. Therefore, a more widely applicable network is needed for sleep staging. This paper introduces MSDC-SSNet, a novel deep learning network for automatic sleep stage classification. MSDC-SSNet transforms two channels of electroencephalogram (EEG) and one channel of electrooculogram (EOG) signals into time-frequency representations to obtain feature sequences at different temporal and frequency scales. An improved Transformer encoder architecture ensures temporal consistency and effectively captures long-term dependencies in EEG and EOG signals. The Multi-Scale Feature Extraction Module (MFEM) employs convolutional layers with varying dilation rates to capture spatial patterns from fine to coarse granularity. It adaptively fuses the weights of features to enhance the robustness of the model. Finally, multiple channel data are integrated to address the heterogeneity between different modalities effectively and alleviate the impact of OSA on sleep stages. We evaluated MSDC-SSNet on three public datasets and our collection of PSG records of 17 OSA patients. It achieved an accuracy of 80.4% on the OSA dataset. It also outperformed the state-of-the-art methods in terms of accuracy, F1 score, and Cohen's Kappa coefficient on the remaining three datasets. The MSDC-SSRNet multi-channel sleep staging architecture proposed in this study enhances widespread system applicability by supplementing inter-channel features. It employs multi-scale attention to extract transition rules between sleep stages and effectively integrates multimodal information. Our method address the limitations of single-channel approaches, enhancing interpretability for clinical applications.

Outlier Handling Strategy of Ensembled-Based Sequential Convolutional Neural Networks for Sleep Stage Classification.

The pivotal role of sleep has led to extensive research endeavors aimed at automatic sleep stage classification. However, existing methods perform poorly when classifying small groups or individuals, and these results are often considered outliers in terms of overall performance. These outliers may introduce bias during model training, adversely affecting feature selection and diminishing model performance. To address the above issues, this paper proposes an ensemble-based sequential convolutional neural network (E-SCNN) that incorporates a clustering module and neural networks. E-SCNN effectively ensembles machine learning and deep learning techniques to minimize outliers, thereby enhancing model robustness at the individual level. Specifically, the clustering module categorizes individuals based on similarities in feature distribution and assigns personalized weights accordingly. Subsequently, by combining these tailored weights with the robust feature extraction capabilities of convolutional neural networks, the model generates more accurate sleep stage classifications. The proposed model was verified on two public datasets, and experimental results demonstrate that the proposed method obtains overall accuracies of 84.8% on the Sleep-EDF Expanded dataset and 85.5% on the MASS dataset. E-SCNN can alleviate the outlier problem, which is important for improving sleep quality monitoring for individuals.

Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks

Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks

Unravelling sleep patterns: Supervised contrastive learning with self-attention for sleep stage classification

Unravelling sleep patterns: Supervised contrastive learning with self-attention for sleep stage classification

Automatic sleep stage classification using deep learning: signals, data representation, and neural networks

In clinical practice, sleep stage classification (SSC) is a crucial step for physicians in sleep assessment and sleep disorder diagnosis. However, traditional sleep stage classification relies on manual work by sleep experts, which is time-consuming and labor-intensive. Faced with this obstacle, computer-aided diagnosis (CAD) has the potential to become an intelligent assistant tool for sleep experts, aiding doctors in the assessment and decision-making process. In fact, in recent years, CAD supported by artificial intelligence, especially deep learning (DL) techniques, has been widely applied in SSC. DL offers higher accuracy and lower costs, making a significant impact. In this paper, we will systematically review SSC research based on DL methods (DL-SSC). We explores DL-SSC from several important perspectives, including signal and data representation, data preprocessing, deep learning models, and performance evaluation. Specifically, this paper addresses three main questions: (1) What signals can DL-SSC use? (2) What are the various methods to represent these signals? (3) What are the effective DL models? Through addressing on these questions, this paper will provide a comprehensive overview of DL-SSC.

An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability

An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability

MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice.

Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and require low computational power but often fail to capture the spatial-temporal context of the data. In contrast, deep neural networks can process raw sleep signals directly and deliver superior performance. However, their overfitting, inconsistent accuracy, and computational cost were the primary drawbacks that limited their end-user acceptance. To address these challenges, we developed a novel neural network model, MLS-Net, which integrates the strengths of neural networks and feature extraction for automated sleep staging in mice. MLS-Net leverages temporal and spectral features from multimodal signals, such as EEG, EMG, and eye movements (EMs), as inputs and incorporates a bidirectional Long Short-Term Memory (bi-LSTM) to effectively capture the spatial-temporal nonlinear characteristics inherent in sleep signals. Our studies demonstrate that MLS-Net achieves an overall classification accuracy of 90.4% and REM state precision of 91.1%, sensitivity of 84.7%, and an F1-Score of 87.5% in mice, outperforming other neural network and feature-based algorithms in our multimodal dataset.

Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients With Disorder of Consciousness

Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients With Disorder of Consciousness

Retraction Note: A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification

The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guestedited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. The publisher has been unable to obtain a current email address for authors Seral O ¨zs ¸en, Cu ¨neyt Yu ¨celbas ¸, S ¸ule Yu ¨celbas ¸and Gu ¨lay Tezel. Authors Serkan Ku ¨c ¸c ¸u ¨ktu ¨rk, Mehmet Dursun and S ¸ebnem Yosunkaya have not responded to correspondence regarding this retraction. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Advancing Sleep Stage Classification with EEG Signal Analysis: LSTM Optimization Using Puffer Fish Algorithm and Explainable AI

In this study, we introduce SleepXAI, a Convolutional Neural Network-Conditional Random Field (CNN-CRF) technique for automatic multi-class sleep stage classification from polysomnography data. SleepXAI enhances classification accuracy while ensuring explainability by highlighting crucial signal segments. Leveraging Long Short-Term Memory (LSTM) networks, it effectively categorizes epileptic EEG signals. Continuous Wavelet Transform (CWT) optimizes signal quality by analyzing eigenvalue characteristics and removing noise. Eigenvalues, which are scalar values indicating the scaling effect on eigenvectors during linear transformations, are used to ensure clean and representative EEG signals. The Puffer Fish Optimization Algorithm fine-tunes LSTM parameters, achieving heightened accuracy by reducing trainable parameters. Evaluation on the Sleep-EDF-20, Sleep-EDF-78, and SHHS datasets shows promising results, with regular accuracy ranging from 85% to 89%. The proposed LSTM-PFOA algorithm demonstrates efficacy for autonomous sleep categorization network development, promising improved sleep stage classification accuracy and facilitating comprehensive health monitoring practices.

SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification.

Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost's implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.

0306 Improving Accessibility and Minimizing Errors: Reliable Sleep Stage Classification Using Wearable Sensors

Abstract Introduction The widespread use of wearable devices has led to the emerging trend to quantify sleep. However, concerns arise due to the potential inaccuracies in these devices, which even can result in adverse daytime consequences. Here, we present an automatized, low-cost, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch classification in four classes (Wake, Light [N1 + N2], Deep, REM) and solely relies on inter-beat-interval (IBI) HRV data. Methods We trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 manually sleep-staged full-night (PSG) recordings and tested the MCNN for classification accuracy using two affordable consumer wearables: an optical heart rate sensor using photoplethysmography (PPG-VS) and an ECG breast belt (H10). Our most recent study tests the model on 136 self-reported poor sleepers equipped with PSG and these wearables. Results Results indicate promising outcomes with high epoch-by-epoch accuracy for ECG (accuracy: 86.3%, κ = 0.79), the H10 ECG (84.4%, κ = 0.76), and the PPG-VS wearable (84.2%, κ = 0.75). The HRV-based model exhibits substantial correlations and agreement with PSG on key sleep parameters, e.g., for Sleep Onset Latency (ρ = 0.82, p < 0.001), Sleep Efficiency (ρ = 0.84, p < 0.001), Total Sleep Time (ρ = 0.94, p < 0.001) and REM sleep (ρ = 0.78, p < 0.001). Notably, the model accurately classifies sleep also among users on heart-affecting or psychoactive medication; a critical aspect for potential clinical use. A first RCTs using this algorithm in our App “Nukkuaa” tested effects of i) daily objective sleep feedback using such sensors in combination with ii) tailored sleep training for insomnia patients using digital cognitive behavioural therapy (dCBT-I) and revealed subjective as well as objective improvements. Conclusion The present validation underscores the potential of automatic sleep stage classification algorithms based on cost-effective wearables and paves the way for scientific and clinical applications which strive for continuous and reliable sleep measurements including epoch-by-epoch sleep stage information as known from traditional PSG. Support (if any) The study was supported by the county of Salzburg (20102-F2002176-FPR) and the FWF/Austrian Science Fund (W 1233-B) for P. Topalidis.

Reliable automatic sleep stage classification based on hybrid intelligence

Reliable automatic sleep stage classification based on hybrid intelligence