Sleep Experts Research Articles

Long-Term Use of Insomnia Medications: An Appraisal of the Current Clinical and Scientific Evidence.

While evidence supports the benefits of medications for the treatment of chronic insomnia, there is ongoing debate regarding their appropriate duration of use. A panel of sleep experts conducted a clinical appraisal regarding the use of insomnia medications, as it relates to the evidence supporting the focus statement, "No insomnia medication should be used on a daily basis for durations longer than 3 weeks at a time". The panelists' assessment was also compared to findings from a national survey of practicing physicians, psychiatrists, and sleep specialists. Survey respondents revealed a wide range of opinions regarding the appropriateness of using the US Food and Drug Administration (FDA)-approved medications for the treatment of insomnia lasting more than 3 weeks. After discussion of the literature, the panel unanimously agreed that some classes of insomnia medications, such as non-benzodiazepines hypnotics, have been shown to be effective and safe for long-term use in the appropriate clinical setting. For eszopiclone, doxepin, ramelteon and the newer class of dual orexin receptor antagonists, the FDA label does not specify that their use should be of a limited duration. Thus, an evaluation of evidence supporting the long-term safety and efficacy of newer non-benzodiazepine hypnotics is timely and should be considered in practice recommendations for the duration of pharmacologic treatment of chronic insomnia.

Improving Children's Sleep Habits Using an Interactive Smartphone App: Community-Based Intervention Study.

Sleep problems are quite common among young children and are often a challenge for parents and a hinderance to children's development. Although behavioral therapy has proven effective in reducing sleep problems in children, a lack of access to professionals who can provide effective support is a major barrier for many caregivers. Therefore, pediatric sleep experts have begun developing apps and web-based services for caregivers. Despite the substantial influence of cultural and familial factors on children's sleep, little effort has gone into developing cultural or family-tailored interventions. This study aimed to examine the effectiveness of the interactive smartphone app "Nenne Navi," which provides culturally and family-tailored suggestions for improving sleep habits in young Japanese children through community-based long-term trials. The study also aimed to investigate the association between app-driven improvements in sleep and mental development in children. This study adopted a community-based approach to recruit individuals from the Higashi-Osaka city (Japan) who met ≥1 of the following eligibility criteria for sleep problems: sleeping after 10 PM, getting <9 hours of nighttime sleep, and experiencing frequent nighttime awakenings. A total of 87 Japanese caregivers with young children (mean 19.50, SD 0.70 months) were recruited and assigned to the app use group (intervention group) or the video-only group (control group). Both groups received educational video content regarding sleep health literacy. The caregivers in the intervention group used the app, which provides family-tailored suggestions, once per month for 1 year. A total of 92% (33/36) of the caregivers in the app use group completed 1 year of the intervention. The participants' overall evaluation of the app was positive. The wake-up time was advanced (base mean 8:06 AM; post mean 7:48 AM; F1,65=6.769; P=.01 and sleep onset latency was decreased (base mean 34.45 minutes; post mean 20.05 minutes; F1,65=23.219; P<.001) significantly in the app use group at the 13th month compared with the video-only group. Moreover, multiple regression analysis showed that decreased social jetlag (β=-0.302; P=.03) and increased sleep onset latency SD (β=.426; P=.02) in children predicted a significant enhancement in the development of social relationships with adults. At 6 months after the completion of the app use, all the caregivers reported continuation of the new lifestyle. The present findings suggest that the app "Nenne Navi" has high continuity in community use and can improve sleep habits in young Japanese children and that interventions for sleep habits of young children may lead to the enhancement of children's social development. Future studies must focus on the effectiveness of the app in other regions with different regional characteristics and neuroscientific investigations on how changes in sleep impact brain development.

An automatic sleep-scoring system in elderly women with osteoporosis fractures using frequency localized finite orthogonal quadrature Fejer Korovkin kernels.

Healthy sleep signifies a good physical and mental state of the body. However, factors such as inappropriate work schedules, medical complications, and others can make it difficult to get enough sleep, leading to various sleep disorders. The identification of these disorders requires sleep stage classification. Visual evaluation of sleep stages is time intensive, placing a significant strain on sleep experts and prone to human errors. As a result, it is crucial to develop machine learning algorithms to score sleep stages to acquire an accurate diagnosis. Hence, a new methodology for automated sleep stage classification is suggested using machine learning and filtering electroencephalogram (EEG) signals. The national sleep research resource's (NSRR) study of osteoporotic fractures (SOF) dataset comprising 453 subjects' polysomnograph (PSG) data is used in this study. Only two unipolar EEG derivations C4-A1 and C3-A2 are employed individually and jointly in this work. The EEG signals are decomposed into sub-bands using a frequency-localized finite orthogonal quadrature Fejer Korovkin wavelet filter bank. The wavelet-based entropy features are extracted from sub-bands. Subsequently, extracted features are classified using machine learning techniques. Our developed model obtained the highest classification accuracy of 81.3%, using an ensembled bagged trees classifier with a 10-fold cross-validation method and Cohen's Kappa coefficient of 0.72. The proposed model is accurate, dependable, and easy to implement and can be employed as an alternative to a PSG-based system at home with minimal resources. It is also ready to be tested on other EEG data to evaluate the sleep stages of healthy and unhealthy subjects.

A real-time automated sleep scoring algorithm to detect refreshing sleep in conscious ventilated critically ill patients

ObjectivesDue to the noisy environment, a very large number of patients admitted to intensive care units (ICUs) suffer from sleep severe disruption. These sleep alterations have been associated with a prolonged need for assisted ventilation or even with death. Sleep scoring in the critically ill is very challenging and requires sleep experts, limiting relevant studies to a few experienced teams. In this context, an automated scoring system would be of interest for researchers. In addition, real-time scoring could be used by nurses to protect patients’ sleep. We devised a sleep scoring algorithm working in real time and compared this automated scoring against visual scoring. MethodsWe analyzed retrospectively 45 polysomnographies previously recorded in non-sedated and conscious ICU patients during their weaning phase. For each patient, one EEG channel was processed, providing automated sleep scoring. We compared total sleep time obtained with visual scoring versus automated scoring. The proportion of sleep episodes correctly identified was calculated. ResultsAutomated total sleep time and visual sleep time were correlated; the automatic system overestimated total sleep time. The median [25th–75th] percentage of sleep episodes lasting more than 10 min detected by algorithm was 100% [73.2 – 100.0]. Median sensitivity was 97.9% [92.5 – 99.9]. ConclusionAn automated sleep scoring system can identify nearly all long sleep episodes. Since these episodes are restorative, this real-time automated system opens the way for EEG-guided sleep protection strategies. Nurses could cluster their non-urgent care procedures, and reduce ambient noise so as to minimize patients’ sleep disruptions.

Sleep Disorders, Comorbidities, Actions, Lower Urinary Tract Dysfunction, and Medications ("Sleep C.A.L.M.") in the evaluation and management of nocturia: A simple approach to a complex diagnosis.

Nocturia arises from a fundamental mismatch between nocturnal urine production, storage capacity, and sleep architecture, which may be driven by abnormalities of the genitourinary tract, but also by sleep disorders, medical diseases, patient actions/lifestyle factors, or medications. This article introduces a novel system for organizing the complex differential diagnosis for nocturia, as proposed by an international collective of practicing urologists, physician specialists, and sleep experts: "Sleep CALM"-Sleep Disorders, Comorbidities, Actions, Lower Urinary Tract Dysfunction, and Medications. Narrative review of current evidence regarding the relevance of each "Sleep CALM" factor to nocturia pathogenesis, evaluation, and management. Nocturia and sleep disorders are highly intertwined and often bidirectional, such that nocturnal awakenings for reasons other than a sensation of bladder fullness should not be used as grounds for exclusion from nocturia treatment, but rather leveraged to broaden therapeutic options for nocturia. Nocturia is an important potential harbinger of several serious medical conditions beyond the genitourinary tract. Urologists should have a low threshold for primary care and medical specialty referral for medical optimization, which carries the potential to significantly improve nocturnal voiding frequency in addition to overall health status. Adverse patient actions/lifestyle factors, lower urinary tract dysfunction, and medication use commonly coexist with disordered sleep and comorbid medical conditions, and may be the primary mediators of nocturia severity and treatment response, or further exacerbate nocturia severity and complicate treatment. "Sleep CALM" provides a memorable and clinically relevant means by which to structure the initial patient history, physical exam, and clinical testing in accordance with current best-practice guidelines for nocturia. Although not intended as an all-encompassing diagnostic tool, the "Sleep CALM" schema may also be useful in guiding individualized ancillary testing, identifying the need for specialty referral and multidisciplinary care, and uncovering first-line treatment targets.

Interpretation and further development of the hypnodensity representation of sleep structure

Objective. The recently-introduced hypnodensity graph provides a probability distribution over sleep stages per data window (i.e. an epoch). This work explored whether this representation reveals continuities that can only be attributed to intra- and inter-rater disagreement of expert scorings, or also to co-occurrence of sleep stage-dependent features within one epoch. Approach. We proposed a simplified model for time series like the ones measured during sleep, and a second model to describe the annotation process by an expert. Generating data according to these models, enabled controlled experiments to investigate the interpretation of the hypnodensity graph. Moreover, the influence of both the supervised training strategy, and the used softmax non-linearity were investigated. Polysomnography recordings of 96 healthy sleepers (of which 11 were used as independent test set), were subsequently used to transfer conclusions to real data. Main results. A hypnodensity graph, predicted by a supervised neural classifier, represents the probability with which the sleep expert(s) assigned a label to an epoch. It thus reflects annotator behavior, and is thereby only indirectly linked to the ratio of sleep stage-dependent features in the epoch. Unsupervised training was shown to result in hypnodensity graph that were slightly less dependent on this annotation process, resulting in, on average, higher-entropy distributions over sleep stages (H unsupervised = 0.41 versus H supervised = 0.29). Moreover, pre-softmax predictions were, for both training strategies, found to better reflect the ratio of sleep stage-dependent characteristics in an epoch, as compared to the post-softmax counterparts (i.e. the hypnodensity graph). In real data, this was observed from the linear relation between pre-softmax N3 predictions and the amount of delta power. Significance. This study provides insights in, and proposes new, representations of sleep that may enhance our comprehension about sleep and sleep disorders.

MCFN: A Multichannel Fusion Network for Sleep Apnea Syndrome Detection.

Sleep apnea syndrome (SAS) is the most common sleep disorder which affects human life and health. Many researchers use deep learning methods to automatically learn the features of physiological signals. However, these methods ignore the different effects of multichannel features from various physiological signals. To solve this problem, we propose a multichannel fusion network (MCFN), which learns the multilevel features through a convolution neural network on different respiratory signals and then reconstructs the relationship between feature channels with an attention mechanism. MCFN effectively fuses the multichannel features to improve the SAS detection performance. We conducted experiments on the Multi-Ethnic Study of Atherosclerosis (MESA) dataset, consisting of 2056 subjects. The experiment results show that our proposed network achieves an overall accuracy of 87.3%, which is better than other SAS detection methods and can better assist sleep experts in diagnosing sleep disorders.

Differentiation of central disorders of hypersomnolence with manual and artificial-intelligence-derived polysomnographic measures.

Differentiation of central disorders of hypersomnolence (DOH) is challenging but important for patient care. This study aimed to investigate whether biomarkers derived from sleep structure evaluated both by manual scoring as well as with artificial intelligence (AI) algorithms allow distinction of patients with different DOH. We included video-polysomnography data of 40 narcolepsy type 1 (NT1), 26 narcolepsy type 2 (NT2), 23 patients with idiopathic hypersomnia (IH) and 54 participants with subjective excessive daytime sleepiness (sEDS). Sleep experts manually scored sleep stages. A previously validated AI algorithm was employed to obtain automatic hypnograms and hypnodensity graphs (where each epoch is represented as a mixture of sleep stage probabilities). One-thousand-three features describing sleep architecture and instability were extracted from manual/automatic hypnogram and hypnodensity graphs. After feature selection, random forest classifiers were trained and tested in a 5-fold-cross-validation scheme to distinguish groups pairwise (NT1-vs-NT2, NT1-vs-IH, …) and single groups from the pooled remaining ones (NT1-vs-rest, NT2-vs-rest,…). The accuracy/F1-score values obtained in the test sets were: 0.74 ± 0.04/0.79 ± 0.05 (NT1-vs-NT2), 0.89 ± 0.09/0.91 ± 0.08 (NT1-vs-IH), 0.93 ± 0.06/0.91 ± 0.07 (NT1-vs-sEDS), 0.88 ± 0.04/0.80 ± 0.07 (NT1-vs-rest), 0.65 ± 0.10/0.70 ± 0.09 (NT2-vs-IH), 0.72 ± 0.12/0.60 ± 0.10 (NT2-vs-sEDS), 0.54 ± 0.19/0.38 ± 0.13 (NT2-vs-rest), 0.57 ± 0.11/0.35 ± 0.18 (IH-vs-sEDS), 0.71 ± 0.08/0.35 ± 0.10 (IH-vs-rest) and 0.76 ± 0.08/0.71 ± 0.13 (sEDS-vs-rest). The results confirm previous findings on sleep instability in patients with NT1 and show that combining manual and automatic AI-based sleep analysis could be useful for better distinction of NT2 from IH, but no precise sleep biomarker of NT2 or IH could be identified. Validation in a larger and multi-centric cohort is needed to confirm these findings.

Understanding primary care diagnosis and management of sleep disturbance for people with dementia or mild cognitive impairment: a realist review protocol

IntroductionThe increasingly ageing population is associated with greater numbers of people living with dementia (PLwD) and mild cognitive impairment (MCI). There are an estimated 55 million PLwD and approximately 6%...

Dreaming in Parasomnias: REM Sleep Behavior Disorder as a Model.

Sleep parasomnias have drawn the interest of sleep experts because they represent a valuable window to directly monitor dream activity and sleep mentation associated with nocturnal events. Indeed, parasomnias and their manifestations are helpful in investigating dream activity and features, overcoming methodological limits that affect dream study. Specifically, REM sleep Behavior Disorder (RBD) is a parasomnia characterized by enacted dream episodes during Rapid Eye Movements (REM) sleep, caused by the loss of physiological atonia. Patients suffering from RBD report a peculiar oneiric activity associated with motor episodes characterized by high Dream Recall Frequency (DRF) and vivid dreams. Additionally, isolated RBD (iRBD) represents a prodromal stage of neurodegeneration preceding the development of α-synucleinopathies. This narrative review aims to combine evidence describing dream activity in RBD and similarities and differences with other NREM parasomnias. Moreover, a special focus has been reserved for those conditions in which RBD is associated with α-synucleinopathies to clarify the potential role of dreams in neurodegenerative processes.

Sleep Experts Say Shift to Permanent Daylight Saving Time Would Have Adverse Health Effects

Sleep Experts Say Shift to Permanent Daylight Saving Time Would Have Adverse Health Effects

A Multilevel Temporal Context Network for Sleep Stage Classification.

Sleep stage classification is essential in diagnosing and treating sleep disorders. Many deep learning models have been proposed to classify sleep stages by automatic learning features and temporal context information. These temporal context features come from the intra-epoch temporal features, which represent the overall morphology of an epoch, and temporal features of adjacent epochs and long epochs, which represent the influence between epochs. However, most existing methods do not fully use the complementarity of the three-level temporal features, resulting in incomplete extracted temporal features. To solve this problem, we propose a multilevel temporal context network (MLTCN) to learn the temporal features from intra-epoch, adjacent epochs, and long epochs, which utilizes the complete temporal features to improve classification accuracy. We evaluate the performance of the proposed model on the Sleep-EDF datasets published in 2013 and 2018. The experimental results show that our MLTCN can achieve an overall accuracy of 84.2% and a kappa coefficient of 0.78 on the Sleep-EDF-2013 dataset. On the larger Sleep-EDF-2018 dataset, the overall accuracy is 81.0%, and a kappa coefficient is 0.74. Our model can better assist sleep experts in diagnosing sleep disorders.

Sleep stage classification in EEG signals using the clustering approach based probability distribution features coupled with classification algorithms

Sleep scoring is one of the primary tasks for the classification of sleep stages in Electroencephalogram (EEG) signals. Manual visual scoring of sleep stages is time-consuming as well as being dependent on the experience of a highly qualified sleep expert. This paper aims to address these issues by developing a new method to automatically classify sleep stages in EEG signals. In this research, a robust method has been presented based on the clustering approach, coupled with probability distribution features, to identify six sleep stages with the use of EEG signals. Using this method, each 30-second EEG signal is firstly segmented into small epochs and then each epoch is divided into 60 sub-segments. Each sub-segment is decomposed into five levels by using a discrete wavelet transform (DWT) to obtain the approximation and detailed coefficient. The wavelet coefficient of each level is clustered using the k-means algorithm. Subsequently, features are extracted based on the probability distribution for each wavelet coefficient. The extracted features then are forwarded to the least squares support vector machine classifier (LS-SVM) to identify sleep stages. Comparisons with several existing methods are also made in this study. The proposed method for the classification of the sleep stages achieves an average accuracy rate of 97.4%. It can be an effective tool for sleep stages classification and can be useful for doctors and neurologists for diagnosing sleep disorders.

Judging Post-Controversy Expertise: Judicial Discretion and Scientific Marginalisation in the Courtroom

ABSTRACT The sexual assault trial of R v Hartman included evidence from a sleep expert who found himself increasingly marginalised within the scientific community. Marginalisation takes place following a scientific controversy, when those considered to be on the losing side find it increasingly difficult to be heard by the community, and in particular, their ideas are removed from core texts in the field. Given a marginalised expert's ambiguous status, and a scientific knowledge deficit on the part of legal actors, on what grounds does a judge base their decision around the evidential value of their testimony? An analysis of the judge's decision in the trial indicates that she evaluated the expert's evidence by employing a version of a socio-technical review that included expectations of scientific rigour based on mechanical objectivity and procedural correctness. Drawing upon these processes and expectations of sound science, the judge had little difficulty evaluating the expert's evidence and finding it unsafe. In particular, she drew attention to the expert's mobilisation of a conspiratorial discursive style, a product of his marginalisation. This supports certain STS claims that legal actors already have tools for evaluating appropriate expertise, and these continue to be the cornerstone of judicial decision-making around expert testimony, even in highly ambiguous situations like post-controversy science.

An attention-based temporal convolutional network for rodent sleep stage classification across species, mutants and experimental environments with single-channel electroencephalogram

Objective. Sleep perturbation by environment, medical procedure and genetic background is under continuous study in biomedical research. Analyzing brain states in animal models such as rodents relies on categorizing electroencephalogram (EEG) recordings. Traditionally, sleep experts have classified these states by visual inspection of EEG signatures, which is laborious. The heterogeneity of sleep patterns complicates the development of a generalizable solution across different species, genotypes and experimental environments. Approach. To realize a generalizable solution, we proposed a cross-species rodent sleep scoring network called CSSleep, a robust deep-learning model based on single-channel EEG. CSSleep starts with a local time-invariant information learning convolutional neural network. The second module is the global transition rules learning temporal convolutional network (TRTCN), stacked with bidirectional attention-based temporal convolutional network modules. The TRTCN simultaneously captures positive and negative time direction information and highlights relevant in-sequence features. The dataset for model evaluation comprises the single-EEG signatures of four cohorts of 16 mice and 8 rats from three laboratories. Main results. In leave-one-cohort-out cross-validation, our model achieved an accuracy of 91.33%. CSSleep performed well on generalization across experimental environments, mutants and rodent species by using single-channel EEG. Significance. This study aims to promote well-standardized cross-laboratory sleep studies to improve our understanding of sleep. Our source codes and supplementary materials will be disclosed later.

Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability

Scoring sleep stages from biological signals is an essential but labor-intensive inspection for sleep diagnosis. The existing automated scoring methods have achieved high accuracy but are not widely applied in clinical practice. In our understanding, the existing methods have failed to establish the trust of sleep experts (e.g., physicians and clinical technologists) due to a lack of ability to explain the evidences/clues for scoring. In this study, we developed a deep-learning-based scoring model with a reasoning mechanism called class activation mapping (CAM) to solve this problem. This mechanism explicitly shows which portions of the signals support our model’s sleep stage decision, and we verified that these portions overlap with the “characteristic waves,” which are evidences/clues used in the manual scoring process. In exchange for the acquisition of explainability, employing CAM makes it difficult to follow some scoring rules. Although we concerned the negative effect of CAM on the scoring accuracy, we have found that the impact is limited. The evaluation experiment shows that the proposed model achieved a scoring accuracy of 86.9%. It is superior to those of some existing methods and the inter-rater reliability among the sleep experts. These results suggest that Sleep-CAM achieved both explainability and required scoring accuracy for practical usage.

Assessment of submentalis muscle activity for sleep-wake classification of healthy individuals and patients with sleep disorders.

This work proposes a method utilizing only the submentalis EMG channel for the classification of sleep and wake states among the healthy individuals and patients with various sleep disorders such as sleep apnea hypopnea syndrome, dyssomnia, etc. We extracted autoregressive model parameters, discrete wavelet transform coefficients, Hjorth's complexity and mobility, relative bandpowers, Poincaré plot descriptors and statistical features from the EMG signal. We also used the energy of each epoch as a feature to distinguish between the sleep and wake states. Mutual information based feature selection approach was considered to obtain the top 25 features which provided maximum accuracy. For classification, we employed an ensemble of decision trees with random undersampling and boosting technique to deal with the class-imbalance problem in the sleep data. We achieved an overall accuracy of about 85% for the healthy population and about 70% on an average across different pathological groups. This work shows the potential of EMG chin activity for sleep analysis. Clinical Relevance- Automatic and reliable sleep-wake classification can reduce the burden of sleep experts in analyzing overnight sleep data (~ 8 hours) and also assist them to diagnose various neurological disorders at an early stage. Utilizing EMG channel provides an easier and convenient long-term recording of data without causing much disturbance in sleepunlike EEG which is inconvenient and hampers the natural sleep.

GTransU-CAP: Automatic labeling for cyclic alternating patterns in sleep EEG using gated transformer-based U-Net framework

Cyclic alternating pattern (CAP) sequences are composed of cycles of alternate activation phases (A-phases) and background phases. CAP A-phases can be further divided into three subtypes, which act as important bio-markers of sleep instability and are also associated with identifiable sleep pathologies. Thus, its accurate detection and identification is of great clinical interest and significance. To release the burden of sleep experts who manually perform this labeling task, several automatic detectors have been proposed, yet the characteristics of CAP have not been fully exploited to achieve a satisfactory performance. In this paper, we propose an automated method to detect A-phases and their subtypes using Transformer-based U-Net framework. In light of the long-span duration of A-phases, our method has intrinsic advantages as U-Net extracts local information while Transformer module provides global dependencies. We also use a curriculum-learning based training strategy to further improve the performance. The method is validated on the publicly available CAP Sleep Database. It obtains average F1 scores of 67.78% and 72.16% on 16 healthy subjects and 30 patients with nocturnal frontal lobe epilepsy respectively for A-phase detection, and the average macro F1-score is 59.5% for multi-class subtype classification. Compared with state-of-the-art methods, the proposed method achieves superior performance in these two CAP labeling tasks.

An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects.

Human life necessitates high-quality sleep. However, humans suffer from a lower quality of life because of sleep disorders. The identification of sleep stages is necessary to predict the quality of sleep. Manual sleep-stage scoring is frequently conducted through sleep experts’ visually evaluations of a patient’s neurophysiological data, gathered in sleep laboratories. Manually scoring sleep is a tough, time-intensive, tiresome, and highly subjective activity. Hence, the need of creating automatic sleep-stage classification has risen due to the limitations imposed by manual sleep-stage scoring methods. In this study, a novel machine learning model is developed using dual-channel unipolar electroencephalogram (EEG), chin electromyogram (EMG), and dual-channel electrooculgram (EOG) signals. Using an optimum orthogonal filter bank, sub-bands are obtained by decomposing 30 s epochs of signals. Tsallis entropies are then calculated from the coefficients of these sub-bands. Then, these features are fed an ensemble bagged tree (EBT) classifier for automated sleep classification. We developed our automated sleep classification model using the Sleep Heart Health Study (SHHS) database, which contains two parts, SHHS-1 and SHHS-2, containing more than 8455 subjects with more than 75,000 h of recordings. The proposed model separated three classes if sleep: rapid eye movement (REM), non-REM, and wake, with a classification accuracy of 90.70% and 91.80% using the SHHS-1 and SHHS-2 datasets, respectively. For the five-class problem, the model produces a classification accuracy of 84.3% and 86.3%, corresponding to the SHHS-1 and SHHS-2 databases, respectively, to classify wake, N1, N2, N3, and REM sleep stages. The model acquired Cohen’s kappa () coefficients as 0.838 with SHHS-1 and 0.86 with SHHS-2 for the three-class classification problem. Similarly, the model achieved Cohen’s of 0.7746 for SHHS-1 and 0.8007 for SHHS-2 in five-class classification tasks. The model proposed in this study has achieved better performance than the best existing methods. Moreover, the model that has been proposed has been developed to classify sleep stages for both good sleepers as well as patients suffering from sleep disorders. Thus, the proposed wavelet Tsallis entropy-based model is robust and accurate and may help clinicians to comprehend and interpret sleep stages efficiently.

Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm.

Background and Objectives: Polysomnography is manually scored by sleep experts. However, manual scoring is a time-consuming and labor-intensive task. The goal of this study was to verify the accuracy of automated sleep-stage scoring based on a deep learning algorithm compared to manual sleep-stage scoring. Materials and Methods: A total of 602 polysomnography datasets from subjects (Male:Female = 397:205) aged 19 to 65 years (mean age, 43.8, standard deviation = 12.2) were included in the study. The performance of the proposed model was evaluated based on kappa value and bootstrapped point-estimate of median percent agreement with a 95% bootstrap confidence interval and R = 1000. The proposed model was trained using 482 datasets and validated using 48 datasets. For testing, 72 datasets were selected randomly. Results: The proposed model exhibited good concordance rates with manual scoring for stages W (94%), N1 (83.9%), N2 (89%), N3 (92%), and R (93%). The average kappa value was 0.84. For the bootstrap method, high overall agreement between the automated deep learning algorithm and manual scoring was observed in stages W (98%), N1 (94%), N2 (92%), N3 (99%), and R (98%) and total (96%). Conclusions: Automated sleep-stage scoring using the proposed model may be a reliable method for sleep-stage classification.