Manual Sleep Staging Research Articles

Retrospective validation of automatic sleep analysis with grey areas model for human-in-the-loop scoring approach.

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.e., in human-in-the-loop applications. To address these challenges, we propose an automatic sleep staging model (aSAGA) that effectively utilises both electroencephalography and electro-oculography channels while incorporating transparency of uncertainty in the decision-making process. We validated the model through extensive retrospective testing using a range of datasets, including open-access, clinical, and research-driven sources. Our channel-wise ensemble model, trained on both electroencephalography and electro-oculography signals, demonstrated robustness and the ability to generalise across various types of sleep recordings, including novel self-applied home polysomnography. Additionally, we compared model uncertainty with human uncertainty in sleep staging and studied various uncertainty mapping metrics to identify ambiguous regions, or "grey areas", that may require manual re-evaluation. The validation of this grey area concept revealed its potential to enhance sleep staging accuracy and to highlight regions in the recordings where sleep experts may struggle to reach a consensus. In conclusion, this study provides a technical basis and understanding of automatic sleep staging uncertainty. Our approach has the potential to improve the integration of automatic sleep staging into clinical practice; however, further studies are needed to test the model prospectively in real-world clinical settings and human-in-the-loop scoring applications.

Time–frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualisation technique

Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine’s (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning. Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages.This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time–frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge “You Snooze, You Win” and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time–frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and ‘interpretable’ features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.

Exploring the potential of a new wearable sleep monitoring device for clinical application

OBJECTIVESSleep is critical to health and quality of life, and the gold-standard assessment method for sleep disorders--polysomnography (PSG)--has struggled to become widely used because of its inherent shortcomings, which portable sleep-monitoring devices are expected to improve. This study aims to validate a new EEG-, EOG-, and EMG-based portable sleep-monitoring device against conventional PSG to explore its feasibility for clinical use. MethodsTwenty participants completed overnight sleep monitoring using conventional polysomnography and the Wearable PolysomnogramTM (WPSG-I). The similarity of the raw signals recorded by the two devices and the agreement rate of their sleep-staging results were compared. ResultsThe raw electroencephalogram (EEG) signals recorded by the two devices were highly similar, and the agreement rates between WPSG-I manual sleep staging and automatic sleep staging with conventional PSG manual sleep staging were 95.8 % (kappa = 0.92) and 89.7 % (kappa = 0.80), respectively. ConclusionThe agreement rate of sleep staging results between the two devices was high. The results of this study demonstrate the validity and feasibility of the WPSG-I for sleep research and clinical applications.

New-Onset Postoperative Atrial Fibrillation and Preoperative Sleep in Cardiac Surgical Patients

BackgroundNew-onset postoperative atrial fibrillation is associated with adverse clinical outcomes in older adults. Poor preoperative sleep quality is a putative modifiable risk factor. The relationships between new-onset postoperative atrial fibrillation and preoperative sleep structure in older adults undergoing elective cardiac surgery were investigated at a single center. MethodsThis was a prespecified substudy within a prospective observational study of perioperative electroencephalographic markers (ClinicalTrials.gov; NCT03291626). We analyzed preoperative sleep recordings from 71 cardiac surgical patients aged ≥60 years without a prior history of atrial fibrillation. Overnight recordings were acquired using a consumer-grade headband and underwent manual sleep staging. Electroencephalographic slow wave activity (power in the 0.5-4 Hz frequency band) was computed in 1-minute intervals for non-rapid eye movement sleep stages. Associations between new-onset postoperative atrial fibrillation incidence and sleep measures were evaluated using univariate logistic regression models and multivariate logistic regression models including age and sex. ResultsNew-onset postoperative atrial fibrillation was present in 22 of 71 (31%) patients. A higher preoperative percentage of total sleep time in non-rapid eye movement stage 1 was associated with new-onset postoperative atrial fibrillation (median difference of 5.4%, P = .0002, Mann-Whitney U-test), independent of age and sex. No associations were observed between new-onset postoperative atrial fibrillation and other sleep metrics, including slow wave activity (all P > .05, Mann-Whitney U-test). ConclusionsExcess preoperative non-rapid eye movement stage 1 sleep, consistent with greater sleep fragmentation, is a potential modifiable target for mitigating new-onset postoperative atrial fibrillation risk in older adults undergoing elective cardiac surgery requiring cardiopulmonary bypass.

“Counting sheep PSG”: EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data

BackgroundProgress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. New methodHere, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. ResultsKey features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. Comparison with existing methodsCounting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. ConclusionsThe field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.

Reliable automatic sleep stage classification based on hybrid intelligence

Sleep staging is a vital aspect of sleep assessment, serving as a critical tool for evaluating the quality of sleep and identifying sleep disorders. Manual sleep staging is a laborious process, while automatic sleep staging is seldom utilized in clinical practice due to issues related to the inadequate accuracy and interpretability of classification results in automatic sleep staging models. In this work, a hybrid intelligent model is presented for automatic sleep staging, which integrates data intelligence and knowledge intelligence, to attain a balance between accuracy, interpretability, and generalizability in the sleep stage classification. Specifically, it is built on any combination of typical electroencephalography (EEG) and electrooculography (EOG) channels, including a temporal fully convolutional network based on the U-Net architecture and a multi-task feature mapping structure. The experimental results show that, compared to current interpretable automatic sleep staging models, our model achieves a Macro-F1 score of 0.804 on the ISRUC dataset and 0.780 on the Sleep-EDFx dataset. Moreover, we use knowledge intelligence to address issues of excessive jumps and unreasonable sleep stage transitions in the coarse sleep graphs obtained by the model. We also explore the different ways knowledge intelligence affects coarse sleep graphs by combining different sleep graph correction methods. Our research can offer convenient support for sleep physicians, indicating its significant potential in improving the efficiency of clinical sleep staging.

SlumberNet: deep learning classification of sleep stages using residual neural networks

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of overall performance (accuracy = 97%; F1 score = 96%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~ 50 × faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet further demonstrates the power of deep learning in mouse sleep research.

Multi-channel frontal EEG – validation on manual sleep staging in a pediatric cohort

Multi-channel frontal EEG – validation on manual sleep staging in a pediatric cohort

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.

EEG-based sleep staging via self-attention based capsule network with Bi-LSTM model

Sleep staging via electroencephalogram is essential for determining the quality of sleep. Manual sleep staging is expensive and time-consuming. Recently, many deep learning-based sleep staging methods are demonstrated to outperform traditional methods. However, most methods do not fully exploit the temporal correlation between features of electroencephalogram signals. In this paper, we propose a self-attention routing-based capsule network with bi-directional long short-term memory model to extract more discriminative features from electroencephalogram signals and improve the accuracy of sleep staging. First, a convolutional neural network is used to extract salient features from the electroencephalogram signal. Second, to learn the transition rules between different sleep epochs, a bi-directional long short-term memory is used to capture the temporal dependence between the encoded electroencephalogram signals. Finally, to fully explore the temporal correlation between the features from the electroencephalogram signals, a self-attention routing-based capsule network is utilized to recode the importance based on the intrinsic temporal similarity of electroencephalogram signals. We evaluated our model by two different single-channel electroencephalogram signals (i.e., Fpz-Cz and Pz-Oz electroencephalogram channels) from two public sleep datasets, named Sleep-EDF-39 and Sleep-EDF-153. Our overall accuracies on the Sleep-EDF-39 and Sleep-EDF-153 datasets are 85.8% and 83.4%, with a kappa of 0.8 and 0.77, respectively. The results show that our proposed method achieves the state-of-the-art level of sleep staging using a single-channel electroencephalogram and offers the possibility of widespread application of capsule networks for sleep staging.

Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals.

Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data. Manual sleep staging requires human expertise, which is occasionally prone to error and variation. In recent times, availability of polysomnography data has aided progress in automatic sleep-stage classification. In this paper, a hybrid deep learning model is proposed for classifying sleep and wake states based on a single-channel electroencephalogram (EEG) signal. The model combines an artificial neural network (ANN) and a convolutional neural network (CNN) trained using mixed-input features. The ANN makes use of statistical features calculated from EEG epochs, and the CNN operates on Hilbert spectrum images generated during each epoch. The proposed method is assessed using single-channel Pz-Oz EEG signals from the Sleep-EDF database Expanded. The classification performance on four randomly selected individuals shows that the proposed model can achieve accuracy of around 96% in classifying between sleep and wake states from EEG recordings.

SleepViTransformer: Patch-based sleep spectrogram transformer for automatic sleep staging

Sleep staging is a crucial aspect of sleep evaluation and disease diagnosis. Numerous automatic schemes have been developed to replace the tedious and expensive task of manual sleep staging. In this paper, we propose SleepViTransformer, a novel scheme that aims to improve the performance of automatic sleep staging. There are three main contributions. (1) A patch-based sleep spectrogram Transformer encoder is proposed for learning more effective feature representations to enhance the automatic sleep staging performance. (2) To reduce the dependence on large amounts of annotated PSG data, cross-modality knowledge from a model pre-trained on image and audio datasets is transferred to SleepViTransformer, significantly improving the model performance. (3) To improve the model robustness under noise and artifacts, a set of PSG augmentations based on the characteristics of the PSG signal is proposed.Experimental results show that SleepViTransformer achieves state-of-the-art performance on four publicly available datasets. On small-scale datasets, SleepViTransformer outperforms the runner-up by 1.8% and 2.5% on SleepEDF-20 and by 1.2% and 1.6% on SleepEDF-78 in terms of accuracy and Cohen’s kappa. SleepViTransformer also performs well on large-scale datasets, outperforming the runner-up by 0.8% and 1.2% on Physio-2018 and 0.4% and 0.2% on SHHS, respectively. The ablation experiments show that both the cross-modality pre-training and PSG augmentation module have positive impacts on improving model performance. To the best of our knowledge, this is the first model to adopt the patch encoding technique from Vision Transformer (ViT) on the sleep PSG spectrogram, showing its eminent potential for PSG signal analysis.

The Virtual Sleep Lab—A Novel Method for Accurate Four-Class Sleep Staging Using Heart-Rate Variability from Low-Cost Wearables

Sleep staging based on polysomnography (PSG) performed by human experts is the de facto "gold standard" for the objective measurement of sleep. PSG and manual sleep staging is, however, personnel-intensive and time-consuming and it is thus impractical to monitor a person's sleep architecture over extended periods. Here, we present a novel, low-cost, automatized, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch four-class sleep staging approach (Wake, Light [N1 + N2], Deep, REM) based solely on inter-beat-interval (IBI) data. Having trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 full-night manually sleep-staged recordings, we tested the MCNN on sleep classification using the IBIs of two low-cost (<EUR 100) consumer wearables: an optical heart rate sensor (VS) and a breast belt (H10), both produced by POLAR®. The overall classification accuracy reached levels comparable to expert inter-rater reliability for both devices (VS: 81%, κ = 0.69; H10: 80.3%, κ = 0.69). In addition, we used the H10 and recorded daily ECG data from 49 participants with sleep complaints over the course of a digital CBT-I-based sleep training program implemented in the App NUKKUAA™. As proof of principle, we classified the IBIs extracted from H10 using the MCNN over the course of the training program and captured sleep-related changes. At the end of the program, participants reported significant improvements in subjective sleep quality and sleep onset latency. Similarly, objective sleep onset latency showed a trend toward improvement. Weekly sleep onset latency, wake time during sleep, and total sleep time also correlated significantly with the subjective reports. The combination of state-of-the-art machine learning with suitable wearables allows continuous and accurate monitoring of sleep in naturalistic settings with profound implications for answering basic and clinical research questions.

Influence of Channel Selection and Subject's Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms.

The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. However, due to the limited availability of EEG sleep datasets, the reliability of existing sleep staging algorithms is questionable. Furthermore, most reported experimental results have been obtained using adult EEG signals; the effectiveness of these algorithms using pediatric EEGs is unknown. In this paper, we conduct an intensive study of two state-of-the-art single-channel EEG-based sleep staging algorithms, namely DeepSleepNet and AttnSleep, using a recently released large-scale sleep dataset collected from 3984 patients, most of whom are children. The paper studies how the performance of these sleep staging algorithms varies when applied on different EEG channels and across different age groups. Furthermore, all results were analyzed within individual sleep stages to understand how each stage is affected by the choice of EEG channel and the participants' age. The study concluded that the selection of the channel is crucial for the accuracy of the single-channel EEG-based automatic sleep staging methods. For instance, channels O1-M2 and O2-M1 performed consistently worse than other channels for both algorithms and through all age groups. The study also revealed the challenges in the automatic sleep staging of newborns and infants (1-52 weeks).

Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography.

The online version contains supplementary material available at 10.1007/s41105-022-00421-5.

Deep Learning Model of Sleep EEG Signal by Using Bidirectional Recurrent Neural Network Encoding and Decoding

Electroencephalogram (EEG) is a signal commonly used for detecting brain activity and diagnosing sleep disorders. Manual sleep stage scoring is a time-consuming task, and extracting information from the EEG signal is difficult because of the non-linear dependencies of time series. To solve the aforementioned problems, in this study, a deep learning model of sleep EEG signal was developed using bidirectional recurrent neural network (BiRNN) encoding and decoding. First, the input signal was denoised using the wavelet threshold method. Next, feature extraction in the time and frequency domains was realized using a convolutional neural network to expand the scope of feature extraction and preserve the original EEG feature information to the maximum extent possible. Finally, the time-series information was mined using the encoding–decoding module of the BiRNN, and the automatic discrimination of the sleep staging of the EEG signal was realized using the SoftMax function. The model was cross-validated using Fpz-Cz single-channel EEG signals from the Sleep-EDF dataset for 19 nights, and the results demonstrated that the proposed model can achieve a high recognition rate and stability.

Classification of Sleep using Polysomnography

Sleep is often recognized as a necessary component of a person’s well-being and is an extremely vital component of a healthy person’s well-being. Sleep is a state in which a person is both physiologically and psychically at ease. The sleep conception is appraised exceedingly unusual, and it has piqued the interest of researchers all around the world. The stages of sleep are examined in order give benefits for studying sleep utilized for the purpose of research. The ability to diagnose sleep disorders has been demonstrated by carefully examining the sleep score system and its various stages. As can be seen, manual sleep stage classification is a time-consuming method that requires adequate measure for sleep professionals to undertake statistical and quantitative analysis. Furthermore, errors and abnormalities in the categoization of the similar facts can occur frequently. As a result, the adoption of an autonomous scoring system to enable trustworthy classification is becoming increasingly popular. The scheduled task teaches you how to apply an automatic system based on EEG (Electroencephalogram), EMG (Electromyogram) and EOG (Electrooculogram), which is known as a polysomnography test or PSG test. For a total of 30 seconds, the recording was measured in length segments. The standard collection of parameters, which gives the distinct stages of sleep, was obtained from several hospitals in sleep laboratories. Sleep waking, Non-Rapid Eye Movement (Stage 1, Stage2, Stage3), and Rapid Eye Movement are the stages. In clinics, the procedure can be highly effective, especially for neurologist detecting sleep problems.

Automated sleep scoring system using multi-channel data and machine learning

Sleep staging is one of the most important parts of sleep assessment and it has an important role in early diagnosis and intervention of sleep disorders. Manual sleep staging requires a specialist and time which can be affected by subjective factors. So that, automatic sleep-scoring method with high accuracy is beneficial. In this work 50 patients sleep data taken from 19 sensors of Philips Alice clinic polysomnography (PSG) device. There is an average of 4772801 data for each individual in a single channel, and approximately 87 million data is processed in 19 channels. Due to the large amount of data, after under sampling technique, dataset is created and Random Forest, Extra Trees and Decision Tree classifiers are applied on it. Although accuracy values vary from one person to another, average of 95.258% for Extra Trees, 95.17% for Random Forest and 91.318% for Decision Tree obtained. Furthermore, precision, recall and F1-score values were also 0.95362, 0.95258 and 0.94568 on average. Beyond the previous works in the area of sleep stage scoring, proposed work differentiated from them by having own database, providing higher accuracy and employing 19 channels. The results showed that the proposed work may alleviate the burden of sleep doctors and speed up sleep scoring.

A Holistic Strategy for Classification of Sleep Stages with EEG.

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification of sleep stages with EEG signals. Though the methodology follows a similar structural flow as proposed in the past works, many advanced and novel techniques are proposed under each category in this work flow. Initially, clustering is applied with the help of hierarchical clustering, spectral clustering, and the proposed principal component analysis (PCA)-based subspace clustering. Then the dimensionality of it is reduced with the help of the proposed singular value decomposition (SVD)-based spectral algorithm and the standard variational Bayesian matrix factorization (VBMF) technique. Then the features are extracted and selected with the two novel proposed techniques, such as the sparse group lasso technique with dual-level implementation (SGL-DLI) and the ridge regression technique with limiting weight scheme (RR-LWS). Finally, the classification happens with the less explored multiclass Gaussian process classification (MGC), the proposed random arbitrary collective classification (RACC), and the deep learning technique using long short-term memory (LSTM) along with other conventional machine learning techniques. This methodology is validated on the sleep EDF database, and the results obtained with this methodology have surpassed the results of the previous studies in terms of the obtained classification accuracy reporting a high accuracy of 93.51% even for the six-classes classification problem.

Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three aspects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring.