831 results match your criteria Sleep Stage Scoring

Operationalizing Ethical Guidance for Ventilator Allocation in Minnesota: Saving the Most Lives or Exacerbating Health Disparities?

Crit Care Explor 2021 Jun 11;3(6):e0455. Epub 2021 Jun 11.

All authors: Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, MN.

A statewide working group in Minnesota created a ventilator allocation scoring system in anticipation of functioning under a Crisis Standards of Care declaration. The scoring system was intended for patients with and without coronavirus disease 2019. There was disagreement about whether the scoring system might exacerbate health disparities and about whether the score should include age. Read More

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Automated scoring of pre-REM sleep in mice with deep learning.

Sci Rep 2021 Jun 10;11(1):12245. Epub 2021 Jun 10.

Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, Jülich, 52428, Germany.

Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. Read More

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Validation Framework for Sleep Stage Scoring in Wearable Sleep Trackers and Monitors with Polysomnography Ground Truth.

Clocks Sleep 2021 May 3;3(2):274-288. Epub 2021 May 3.

Department of Biomedical Engineering, North Dakota State University, Fargo, ND 58108, USA.

The rapid growth of point-of-care polysomnographic alternatives has necessitated standardized evaluation and validation frameworks. The current average across participant validation methods may overestimate the agreement between wearable sleep tracker devices and polysomnography (PSG) systems because of the high base rate of sleep during the night and the interindividual difference across the sampling population. This study proposes an evaluation framework to assess the aggregating differences of the sleep architecture features and the chronologically epoch-by-epoch mismatch of the wearable sleep tracker devices and the PSG ground truth. Read More

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WaveSleepNet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake.

J Neurosci Methods 2021 May 28;360:109224. Epub 2021 May 28.

Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, USA. Electronic address:

Background: Recent advancement in deep learning provides a pivotal opportunity to potentially supplement or supplant the limiting step of manual sleep scoring.

New Method: In this paper, we characterize the WaveSleepNet (WSN), a deep convolutional neural network (CNN) that uses wavelet transformed images of mouse EEG/EMG signals to autoscore sleep and wake.

Results: WSN achieves an epoch by epoch mean accuracy of 0. Read More

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Single-channel EEG classification of sleep stages based on REM microstructure.

Healthc Technol Lett 2021 Jun 20;8(3):58-65. Epub 2021 Apr 20.

Department of Neuroscience "Rita Levi Montalcini" Università degli Studi di Torino Torino Italy.

Rapid-eye movement (REM) sleep, or paradoxical sleep, accounts for 20-25% of total night-time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and related activities would help to improve their quality of life. There is a need to accurately classify REM and the other stages of sleep in order to properly identify and monitor parasomnias. Read More

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Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier.

Neurosci Res 2021 May 11. Epub 2021 May 11.

School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China.

Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy because any errors in the scoring in the patient's sleep EEG recordings can cause serious problems. The aim of this research is to develop a new automatic method for detecting the most important characteristics in sleep stage 2 such as k-complexes based on multi-domain features. Read More

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[The role of biomarkers in the detection of the OSA syndrome. A narrative review of the literature].

Rev Mal Respir 2021 May 3;38(5):455-465. Epub 2021 May 3.

Chef de service et maître de stages, Département d'anesthésie-réanimation, Clinique Saint-Luc-de-Bouge, Namur, Belgique. Electronic address:

Introduction: Obstructive sleep apnoea (OSA) is a common sleep-related breath disorder associated with cardiovascular and cerebrovascular complications, such as hypertension, arrhythmia, coronary artery disease and stroke. Unfortunately, OSA is underdiagnosed.

Background: Because of its clinical and therapeutic variability, OSA could benefit a personalized medicine approach. Read More

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Determination of the sleep-wake pattern and feasibility of NREM/REM discrimination using the non-invasive piezoelectric system in rats.

J Sleep Res 2021 May 3:e13373. Epub 2021 May 3.

Unité Fatigue et Vigilance, Institut de Recherche Biomédicale des Armées (IRBA), Brétigny-sur-Orge, France.

The piezoelectric cage-floor sensors have been used to successfully dissect sleep patterns in mice based on signal features related to respiration and body movements. We studied performance of the piezoelectric system to quantify the sleep-wake pattern in the rat over 7 days of recording compared with a visual electroencephalogram/electromyogram scoring, and under two light/dark (LD12:12 and LD16:8) photoperiods leading to change in the 24-hr sleep characteristics (N = 7 per group). The total sleep time (%/24 hr) over the 7 days recording and hourly sleep time over the last 24-hr recording were not statistically different between methods under the two photoperiods. Read More

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STQS: Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoring.

Artif Intell Med 2021 Apr 27;114:102038. Epub 2021 Feb 27.

University of Twente, Netherlands; University of Duisburg-Essen, Germany.

Sleep scoring is an important step for the detection of sleep disorders and usually performed by visual analysis. Since manual sleep scoring is time consuming, machine-learning based approaches have been proposed. Though efficient, these algorithms are black-box in nature and difficult to interpret by clinicians. Read More

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Temperature rhythms and ICU sleep: the TRIS study.

Minerva Anestesiol 2021 Apr 14. Epub 2021 Apr 14.

Thoracic Medicine Royal Brisbane and Women's Hospital, Herston, Australia.

Background: Core body temperature (CBT) patterns associated with sleep have not been described in the critically ill. This study aimed to characterise night-time sleep and its relationship to CBT in ICU patients.

Methods: A prospective study was performed in a 27-bed tertiary adult intensive care unit of 20 mechanically ventilated patients in the weaning stage of their critical illness. Read More

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Attention-based LSTM for Non-Contact Sleep Stage Classification using IR-UWB radar.

IEEE J Biomed Health Inform 2021 Apr 13;PP. Epub 2021 Apr 13.

Manual scoring of sleep stages from polysomnography (PSG) records is essential to understand the sleep quality and architecture. Since the PSG requires specialized personnel, a lab environment, and uncomfortable sensors, non-contact sleep staging methods based on machine learning techniques have been investigated over the past years. In this study, we propose an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model for automatic sleep stage scoring using an impulse-radio ultra-wideband (IR-UWB) radar which can remotely detect vital signs. Read More

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Automatic Detection of Microsleep Episodes With Deep Learning.

Front Neurosci 2021 24;15:564098. Epub 2021 Mar 24.

Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Read More

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Scoring of large muscle group movements during sleep: An International Restless Legs Syndrome Study Group (IRLSSG) position statement.

Sleep 2021 Apr 9. Epub 2021 Apr 9.

University of Illinois School of Medicine, Carle Illinois College of Medicine, and Carle Foundation Hospital, Urbana, IL, USA.

There is a gap in the manuals for scoring sleep-related movements because of the absence of rules for scoring large movements. A taskforce of the International Restless Legs Syndrome Study Group elaborated rules that define the detection and quantification of movements involving large muscle groups. Consensus on each of the criteria in this paper was reached by testing the presence of consensus on a first proposal; if no consensus was achieved, the concerns were considered and used to modify the proposal. Read More

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Sleep scoring based on video-electroencephalography monitoring in an Epileptology Unit: Comparison with polysomnography.

J Sleep Res 2021 Apr 6:e13332. Epub 2021 Apr 6.

Department of Clinical Neurophysiology, AP-HP, Hôpital Pitié-Salpêtrière-Charles Foix, Paris, France.

The aim of the study was to compare the performance of video- electroencephalography (EEG) monitoring and standard polysomnography for sleep scoring in an Epileptology Unit. We calculated the level of agreement between two methods of sleep scoring, using either 27-electrode video-EEG or polysomnography for 1 night in 22 patients admitted to our Epileptology Unit. Independent experts manually scored sleep using the American Academy of Sleep Medicine 2017 guidelines. Read More

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Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals.

Int J Environ Res Public Health 2021 03 17;18(6). Epub 2021 Mar 17.

School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Read More

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Analysis and visualization of sleep stages based on deep neural networks.

Neurobiol Sleep Circadian Rhythms 2021 May 12;10:100064. Epub 2021 Mar 12.

Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Germany.

Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. Read More

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Transfer learning from ECG to PPG for improved sleep staging from wrist-worn wearables.

Physiol Meas 2021 Mar 24. Epub 2021 Mar 24.

Department of Biomedical Informatics, Emory University, Atlanta, Georgia, UNITED STATES.

Objective: To develop a sleep staging method from wrist-worn accelerometry and the photoplethysmogram (PPG) by leveraging transfer learning from a large electrocardiogram (ECG) database.

Approach: In previous work, we developed a deep convolutional neural network for sleep staging from ECG using the cross-spectrogram of ECG-derived respiration and instantaneous beat intervals, heart rate variability metrics, spectral characteristics, and signal quality measures derived from 5,793 subjects in Sleep Heart Health Study (SHHS). We updated the weights of this model by transfer learning using PPG data derived from the Empatica E4 wristwatch worn by 105 subjects in the `Emory Twin Study Follow-up' (ETSF) database, for whom overnight polysomnographic (PSG) scoring was available. Read More

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Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography.

Front Physiol 2021 3;12:628502. Epub 2021 Mar 3.

School of Science, China Pharmaceutical University, Nanjing, China.

This study centers on automatic sleep staging with a single channel electroencephalography (EEG), with some significant findings for sleep staging. In this study, we proposed a deep learning-based network by integrating attention mechanism and bidirectional long short-term memory neural network (AT-BiLSTM) to classify wakefulness, rapid eye movement (REM) sleep and non-REM (NREM) sleep stages N1, N2 and N3. The AT-BiLSTM network outperformed five other networks and achieved an accuracy of 83. Read More

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A Systematic Review of Sensing Technologies for Wearable Sleep Staging.

Syed Anas Imtiaz

Sensors (Basel) 2021 Feb 24;21(5). Epub 2021 Feb 24.

Wearable Technologies Lab, Imperial College London, London SW7 2AZ, UK.

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Read More

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February 2021

Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm.

J Clin Sleep Med 2021 Jun;17(6):1237-1247

Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.

Study Objectives: The objective of this study was to evaluate interrater reliability between manual sleep stage scoring performed in 2 European sleep centers and automatic sleep stage scoring performed by the previously validated artificial intelligence-based Stanford-STAGES algorithm.

Methods: Full night polysomnographies of 1,066 participants were included. Sleep stages were manually scored in Berlin and Innsbruck sleep centers and automatically scored with the Stanford-STAGES algorithm. Read More

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Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography.

Sleep Breath 2021 Feb 16. Epub 2021 Feb 16.

Faculty of Sleep Medicine and Telemedicine, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany.

Purpose: In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB). Read More

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February 2021

Obstructive sleep apnea and sudden unexpected death in epilepsy in unselected patients with epilepsy: are they associated?

Sleep Breath 2021 Feb 13. Epub 2021 Feb 13.

Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand.

Purpose: We aimed to determine (1) the frequency of high-risk sudden unexpected death in epilepsy (SUDEP) in patients with epilepsy who have had obstructive sleep apnea (OSA) in different stages of sleep using the revised SUDEP risk inventory (rSUDEP-7) score instrument and (2) the factors associated with high risk SUDEP in patients with epilepsy who have had OSA.

Methods: We conducted a cross-sectional study of consecutive subjects who are more than 15 years old without known sleep disorders, recruited from a single epilepsy clinic in a tertiary care facility. Participants underwent polysomnography. Read More

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February 2021

Abnormal Brain Network Topology During Non-rapid Eye Movement Sleep and Its Correlation With Cognitive Behavioral Abnormalities in Narcolepsy Type 1.

Front Neurol 2020 11;11:617827. Epub 2021 Jan 11.

Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) were applied to investigate the abnormalities in the topological characteristics of functional brain networks during non-rapid eye movement(NREM)sleep. And we investigated its relationship with cognitive abnormalities in patients with narcolepsy type 1 (NT1) disorder in the current study. The Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) and EEG-fMRI were applied in 25 patients with NT1 and 25 age-matched healthy controls. Read More

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January 2021

Sleep modelled as a continuous and dynamic process predicts healthy ageing better than traditional sleep scoring.

Sleep Med 2021 01 5;77:136-146. Epub 2020 Dec 5.

Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.

Background: In current clinical practice, sleep is manually scored in discrete stages of 30-s duration. We hypothesize that modelling sleep automatically as continuous and dynamic process predicts healthy ageing better than traditional scoring.

Methods: Sleep electroencephalography of 15 young healthy subjects (aged ≤40 years) was used to train the modelling method. Read More

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January 2021

Ear-EEG for sleep assessment: a comparison with actigraphy and PSG.

Sleep Breath 2020 Nov 21. Epub 2020 Nov 21.

Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, Building 5125, 8200, Aarhus, Denmark.

Purpose: To assess automatic sleep staging of three ear-EEG setups with different electrode configurations and compare performance with concurrent polysomnography and wrist-worn actigraphy recordings.

Methods: Automatic sleep staging was performed for single-ear, single-ear with ipsilateral mastoid, and cross-ear electrode configurations, and for actigraphy data. The polysomnography data were manually scored and used as the gold standard. Read More

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November 2020

Multi-Branch Convolutional Neural Network for Automatic Sleep Stage Classification with Embedded Stage Refinement and Residual Attention Channel Fusion.

Sensors (Basel) 2020 Nov 18;20(22). Epub 2020 Nov 18.

College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China.

Automatic sleep stage classification of multi-channel sleep signals can help clinicians efficiently evaluate an individual's sleep quality and assist in diagnosing a possible sleep disorder. To obtain accurate sleep classification results, the processing flow of results from signal preprocessing and machine-learning-based classification is typically employed. These classification results are refined based on sleep transition rules. Read More

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November 2020

Automatic sleep scoring: A deep learning architecture for multi-modality time series.

J Neurosci Methods 2021 01 4;348:108971. Epub 2020 Nov 4.

School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014, Jyväskylä, Finland; School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian, China; Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, 116024, Dalian, China.

Background: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings.

Method: The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Read More

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January 2021

Sleep-dependent memory consolidation in children with self-limited focal epilepsies.

Epilepsy Behav 2020 12 28;113:107513. Epub 2020 Oct 28.

Children's Research Center, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland; Department of Neurology, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland.

Objective: Children with self-limited focal epilepsies of childhood (SLFE) are known to show impaired memory functions, particularly in the verbal domain. Interictal epileptiform discharges (IED) in these epilepsies are more pronounced in nonrapid eye movement (NREM) sleep. Nonrapid eye movement sleep is crucial for consolidation of newly-encoded memories. Read More

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December 2020

A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network.

Eur Neurol 2020 29;83(5):468-486. Epub 2020 Oct 29.

Department of Biomedical Informatics and Department of Neuroscience, The Ohio State University, Columbus, Ohio, USA.

Introduction: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of sleep-EEG signals using wavelet transform and recurrent neural network (RNN).

Methods: Two RNNs were designed and trained separately based on a database of classical guitar pieces and Kurdish tanbur Makams using a long short-term memory model. Read More

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Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders.

J Clin Sleep Med 2021 Mar;17(3):393-402

Center for Sleep Medicine and Respiratory Diseases, Croix-Rousse Hospital, Lyon, France.

Study Objectives: To assess the performance of the single-channel automatic sleep staging (AS) software ASEEGA in adult patients diagnosed with various sleep disorders.

Methods: Sleep recordings were included of 95 patients (38 women, 40.5 ± 13. Read More

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