774 results match your criteria Sleep Stage Scoring


Convolution-and Attention-Based Neural Network for Automated Sleep Stage Classification.

Int J Environ Res Public Health 2020 Jun 10;17(11). Epub 2020 Jun 10.

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

Analyzing polysomnography (PSG) is an effective method for evaluating sleep health; however, the sleep stage scoring required for PSG analysis is a time-consuming effort for an experienced medical expert. When scoring sleep epochs, experts pay attention to find specific signal characteristics (e.g. Read More

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http://dx.doi.org/10.3390/ijerph17114152DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313068PMC
June 2020
2.063 Impact Factor

Expert-level automated sleep staging of long-term scalp EEG recordings using deep learning.

Sleep 2020 Jun 1. Epub 2020 Jun 1.

Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Study Objectives: Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings.

Methods: Using a clinical dataset of polysomnograms from 6,431 patients (MGH-PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network (CNN) for feature extraction, followed by a recurrent neural network (RNN) that extracts temporal dependencies of sleep stages. Read More

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http://dx.doi.org/10.1093/sleep/zsaa112DOI Listing

Reliability of Family Dogs' Sleep Structure Scoring Based on Manual and Automated Sleep Stage Identification.

Animals (Basel) 2020 May 26;10(6). Epub 2020 May 26.

Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, 1117 Budapest, Hungary.

Non-invasive polysomnography recording on dogs has been claimed to produce data comparable to those for humans regarding sleep macrostructure, EEG spectra and sleep spindles. While functional parallels have been described relating to both affective (e.g. Read More

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http://dx.doi.org/10.3390/ani10060927DOI Listing

Neonatal EEG sleep stage classification based on deep learning and HMM.

J Neural Eng 2020 Jun 25;17(3):036031. Epub 2020 Jun 25.

Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran. Department of Electrical Engineering, Persian Gulf University, Bushehr, Iran.

Objective: Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates.

Approach: The classification performance was evaluated on quiet sleep (QS) and active sleep (AS) stages, each with two sub-states, using multichannel EEG data recorded from sixteen neonates with postmenstrual age of 38-40 weeks. Read More

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http://dx.doi.org/10.1088/1741-2552/ab965aDOI Listing

Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.

Sleep 2020 May 21. Epub 2020 May 21.

Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.

Study Objectives: Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea, OSA) but relies on labor-intensive EEG-based manual scoring. Read More

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http://dx.doi.org/10.1093/sleep/zsaa098DOI Listing

The Dreem Headband compared to Polysomnography for EEG Signal Acquisition and Sleep Staging.

Sleep 2020 May 20. Epub 2020 May 20.

French Armed Forces Biomedical Research Institute (IRBA), Fatigue and Vigilance Unit, Bretigny sur Orge, France; EA 7330 VIFASOM, Paris Descartes University, Paris, France.

Objectives: The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical for advancing sleep science. The aim of this study was to assess the signal acquisition and the performance of the automatic sleep staging algorithms of a reduced-montage dry-EEG device (Dreem headband, DH) compared to the gold-standard polysomnography (PSG) scored by 5 sleep experts.

Methods: Twenty-five subjects who completed an overnight sleep study at a sleep center while wearing both a PSG and the DH simultaneously have been included in the analysis. Read More

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http://dx.doi.org/10.1093/sleep/zsaa097DOI Listing
May 2020
4.591 Impact Factor

Automatic sleep-stage scoring based on photoplethysmographic signals.

Physiol Meas 2020 Jun 30;41(6):065008. Epub 2020 Jun 30.

School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275 People's Republic of China.

Objective: Sleep-stage scoring is important for sleep-quality evaluation and the diagnosis of related diseases. In this study, an automatic sleep-stage scoring method using photoplethysmographic (PPG) signals was proposed.

Approach: To construct the classification model, we extracted 14 time-domain features, 17 frequency-domain features, and 20 pulse rate variability (PRV) features along with four SpO features from PPG signals. Read More

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http://dx.doi.org/10.1088/1361-6579/ab921dDOI Listing

Dynamics of sleep: Exploring critical transitions and early warning signals.

Comput Methods Programs Biomed 2020 Mar 21;193:105448. Epub 2020 Mar 21.

Department of Psychology, University of Amsterdam, the Netherlands.

Background And Objectives: In standard practice, sleep is classified into distinct stages by human observers according to specific rules as for instance specified in the AASM manual. We here show proof of principle for a conceptualization of sleep stages as attractor states in a nonlinear dynamical system in order to develop new empirical criteria for sleep stages.

Methods: EEG (single channel) of two healthy sleeping participants was used to demonstrate this conceptualization. Read More

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http://dx.doi.org/10.1016/j.cmpb.2020.105448DOI Listing

Beyond K-complex binary scoring during sleep: Probabilistic classification using deep learning.

Sleep 2020 Apr 16. Epub 2020 Apr 16.

Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University.

Background: K-complexes (KCs) are a recognized EEG marker of sensory-processing and a defining feature of sleep stage 2. KC frequency and morphology may also be reflective of sleep quality, aging and a range of sleep and sensory processing deficits. However, manual scoring of K-complexes is impractical, time-consuming and thus costly and currently not well-standardized. Read More

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http://dx.doi.org/10.1093/sleep/zsaa077DOI Listing

Neonatal sleep stage identification using long short-term memory learning system.

Med Biol Eng Comput 2020 Jun 12;58(6):1383-1391. Epub 2020 Apr 12.

Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.

Neonatal sleep analysis at the neonatal intensive care units (NICU) is critical for the diagnosis of any brain growth risks during the early stages of life. In this paper, an investigation is carried out on the use of a long short-term memory (LSTM) learning system in automatic sleep stage scoring in neonates. The developed algorithm automatically classifies sleep stages based on inputs from a single channel EEG recording. Read More

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http://dx.doi.org/10.1007/s11517-020-02169-xDOI Listing

A hybrid double-density dual-tree discrete wavelet transformation and marginal Fisher analysis for scoring sleep stages from unprocessed single-channel electroencephalogram.

Quant Imaging Med Surg 2020 Mar;10(3):766-778

Department of Electrical Engineering and Computer Sciences, University of California, Irvine, CA, USA.

Background: We demonstrate an innovative approach of automated sleep recording formed on the electroencephalogram (EEG) with one channel.

Methods: In this study, double-density dual-tree discrete wavelet transformation (DDDTDWT) was used for decomposing the image, and marginal Fisher analysis (MFA) was used for reducing the dimension. A proposed model on unprocessed EEG models was used on monitored training of 5-group sleep phase forecasting. Read More

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http://dx.doi.org/10.21037/qims.2020.02.01DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7136739PMC

Hematopoietic stem cell transplantation in mucopolysaccharidosis type IIIA: A case description and comparison with a genotype-matched control group.

Mol Genet Metab Rep 2020 Jun 23;23:100578. Epub 2020 Mar 23.

Department of Pediatrics, University Medical Center Hamburg Eppendorf, Hamburg, Germany.

Background: Mucopolysaccharidosis type IIIA (MPS IIIA, Sanfilippo A syndrome) is a chronic progressive neurodegenerative storage disorder caused by a deficiency of lysosomal sulfamidase. The clinical hallmarks are sleep disturbances, behavioral abnormalities and loss of cognitive, speech and motor abilities. Affected children show developmental slowing from the second year of life, dementia occurs by the age of 5 years followed by death in the second decade of life. Read More

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http://dx.doi.org/10.1016/j.ymgmr.2020.100578DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093801PMC

Clinical and Prognostic Values of ALBI Score in Patients With Acute Heart Failure.

Heart Lung Circ 2019 Dec 23. Epub 2019 Dec 23.

Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan; Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio, USA.

Background: Although liver dysfunction is one of the common complications in patients with acute heart failure (AHF), no integrated marker has been defined. The albumin-bilirubin (ALBI) score has recently been proposed as a novel, clinically-applicable scoring system for liver dysfunction. We investigated the utility of the ALBI score in patients with AHF compared to that for a preexisting liver dysfunction score, the Model of End-Stage Liver Disease Excluding prothrombin time (MELD XI) score. Read More

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http://dx.doi.org/10.1016/j.hlc.2019.12.003DOI Listing
December 2019
1.172 Impact Factor

Sleep structure in sleep bruxism: A polysomnographic study including bruxism activity phenotypes across sleep stages.

J Sleep Res 2020 Mar 11:e13028. Epub 2020 Mar 11.

Department of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology, Wroclaw Medical University, Wroclaw, Poland.

The aim of the study was to assess sleep structure, phenotypes related to bruxism activity and basic respiratory parameters among a large group of participants with sleep bruxism and without obstructive sleep apnea. Adult participants with clinical suspicion of sleep bruxism and with no other significant medical history were recruited. Video-polysomnography was performed to detect masseter muscles activity. Read More

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http://dx.doi.org/10.1111/jsr.13028DOI Listing

Long Short-Term Memory Networks for Unconstrained Sleep Stage Classification using Polyvinylidene Fluoride Film Sensor.

IEEE J Biomed Health Inform 2020 Mar 9. Epub 2020 Mar 9.

Sleep stage scoring is the first step towards quantitative analysis of sleep using polysomnography (PSG) recordings. However, although PSG is a gold standard method for assessing sleep, it is obtrusive and difficult to apply for long-term sleep monitoring. Further, because human experts manually classify sleep stages, it is time-consuming and exhibits inter-rater variability. Read More

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http://dx.doi.org/10.1109/JBHI.2020.2979168DOI Listing

Nocturnal Rapid Eye Movement Sleep Without Atonia can Be a Diagnostic Parameter in Differentiating Narcolepsy Type 1 From Type 2.

J Clin Neurophysiol 2020 Feb 13. Epub 2020 Feb 13.

Hacettepe University, School of Medicine, Department of Neurology, Ankara, Turkey. Dr. Yon is now with the Department of Neurology, Faculty of Medicine, Ankara Yildirim Beyazit University, Ankara City Hospital, Ankara, Turkey.

Purpose: We aimed to compare rapid eye movement sleep without atonia (RSWA), tonic RSWA, and phasic RSWA indices during polysomnography as a potential biomarker between narcolepsy type 1 and type 2.

Methods: Medical files, polysomnography, and multiple sleep latency tests of patients with narcolepsy were evaluated retrospectively. A total of three adolescents and 31 adult patients were included. Read More

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http://dx.doi.org/10.1097/WNP.0000000000000688DOI Listing
February 2020

Influence of Postconcussion Sleep Duration on Concussion Recovery in Collegiate Athletes.

Clin J Sport Med 2020 Mar;30 Suppl 1:S29-S35

Department of Kinesiology, University of Georgia, Athens, Georgia.

Objective: To determine whether decreased sleep duration postconcussion influences days to asymptomatic and assessment of performance throughout recovery.

Design: Prospective.

Setting: Institutional Clinical Research Laboratory. Read More

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http://dx.doi.org/10.1097/JSM.0000000000000538DOI Listing

Concordance between current American Academy of Sleep Medicine and Centers for Medicare and Medicare scoring criteria for obstructive sleep apnea in hospitalized persons with traumatic brain injury: a VA TBI Model System study.

J Clin Sleep Med 2020 Jun;16(6):879-888

Research Department, Craig Hospital, Englewood, Colorado.

Study Objectives: The objective of this study was to compare obstructive sleep apnea (OSA), demographic, and traumatic brain injury (TBI) characteristics across the American Academy of Sleep Medicine (AASM) and Centers for Medicare and Medicare (CMS) scoring rules in moderate to severe TBI undergoing inpatient neurorehabilitation.

Methods: This is a secondary analysis from a prospective clinical trial of sleep apnea at 6 TBI Model System study sites (n = 248). Scoring was completed by a centralized center using both the AASM and CMS criteria for OSA. Read More

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http://dx.doi.org/10.5664/jcsm.8352DOI Listing

A novel sleep stage scoring system: Combining expert-based features with the generalized linear model.

J Sleep Res 2020 Feb 7:e12991. Epub 2020 Feb 7.

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.

In this study, we aim to automate the sleep stage scoring process of overnight polysomnography (PSG) data while adhering to expert-based rules. We developed a sleep stage scoring algorithm utilizing the generalized linear modelling (GLM) framework and extracted features from electroencephalogram (EEG), electromyography (EMG) and electrooculogram (EOG) signals based on predefined rules of the American Academy of Sleep Medicine (AASM) Manual for Scoring Sleep. Specifically, features were computed in 30-s epochs in the time and frequency domains of the signals and were then used to model the probability of an epoch being in each of five sleep stages: N3, N2, N1, REM or Wake. Read More

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http://dx.doi.org/10.1111/jsr.12991DOI Listing
February 2020

Effectiveness of Sleep Apnea Detection Based on One vs. Two Symmetrical EEG Channels.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:4056-4059

Typically, two symmetrical EEG channels are recorded during polysomnography (PSG). As a rule, only the recommended channel is used for sleep stage scoring or sleep apnea detection, and the other for backup. Concurrently, there are many works demonstrating the asymmetry in brain activity. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8856632DOI Listing

A Transition Probability Based Classification Model for Enhanced N1 Sleep stage Identification During Automatic Sleep Stage Scoring.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:3641-3644

Automatic sleep staging provides a cheaper, faster and more accessible alternative for evaluating sleep patterns and quality compared with manual hypnogram scoring performed by a clinician. Traditionally, classification methods treat sleep stages independently of their temporal order, despite sleep patterns themselves being highly sequential. Such independent sleep stage classification can result in poor sensitivity and precision, in particular when attempting to classify the sleep stage N1, otherwise known as the transition stage of sleep which links periods of wakefulness to periods of deep sleep. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8856710DOI Listing

Scalable automatic sleep staging in the era of Big Data.

Conf Proc IEEE Eng Med Biol Soc 2019 07;2019:2265-2268

Numerous automatic sleep staging approaches have been proposed to provide an eHealth alternative to the current gold-standard - hypnogram scoring by human experts. However, a majority of such studies exploit data of limited scale, which compromises both the validation and the reproducibility and transferability of such automatic sleep staging systems in real clinical settings. In addition, the computational issues and physical meaningfulness of the analysis are typically neglected, yet affordable computation is a key criterion in Big Data analytics. Read More

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http://dx.doi.org/10.1109/EMBC.2019.8857356DOI Listing

Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data.

Sleep Breath 2020 Jun 14;24(2):581-590. Epub 2020 Jan 14.

Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.

Purpose: To develop an automated framework for sleep stage scoring from PSG via a deep neural network.

Methods: An automated deep neural network was proposed by using a multi-model integration strategy with multiple signal channels as input. All of the data were collected from one single medical center from July 2017 to April 2019. Read More

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http://dx.doi.org/10.1007/s11325-019-02008-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289784PMC

[High-resolution Peripheral Quantitative Computed Tomography for the Assessment of Bone Strength and Structure in Obstructive Sleep Apnea Patients].

Zhongguo Yi Xue Ke Xue Yuan Xue Bao 2019 Dec;41(6):761-771

Department of Respiratory and Critical Care Medicine, PUMC Hospital,CAMS and PUMC,Beijing 100730,China.

To evaluate the bone strength and structure of patients with obstructive sleep apnea(OSA)by the high-resolution peripheral quantitative computed tomography(HR-pQCT)and to explore the relationship between OSA and osteoporosis. Male patients who visited the Sleep Respiratory Center of our hospital from August 2017 to January 2019 were consecutively recruited.Clinical data including the results of Epworth sleep scale(ESS)scoring and overnight polysomnography were collected. Read More

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http://dx.doi.org/10.3881/j.issn.1000-503X.11041DOI Listing
December 2019

Accurate Deep Learning-Based Sleep Staging in a Clinical Population With Suspected Obstructive Sleep Apnea.

IEEE J Biomed Health Inform 2020 Jul 19;24(7):2073-2081. Epub 2019 Dec 19.

The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overcome this shortcoming, we aimed to develop an accurate deep learning approach for automatic classification of sleep stages and to study the effect of OSA severity on the classification accuracy. Read More

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http://dx.doi.org/10.1109/JBHI.2019.2951346DOI Listing

Robust, automated sleep scoring by a compact neural network with distributional shift correction.

PLoS One 2019 13;14(12):e0224642. Epub 2019 Dec 13.

Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. Read More

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0224642PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6910668PMC

Supervised and unsupervised machine learning for automated scoring of sleep-wake and cataplexy in a mouse model of narcolepsy.

Sleep 2020 May;43(5)

Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA.

Despite commercial availability of software to facilitate sleep-wake scoring of electroencephalography (EEG) and electromyography (EMG) in animals, automated scoring of rodent models of abnormal sleep, such as narcolepsy with cataplexy, has remained elusive. We optimize two machine-learning approaches, supervised and unsupervised, for automated scoring of behavioral states in orexin/ataxin-3 transgenic mice, a validated model of narcolepsy type 1, and additionally test them on wild-type mice. The supervised learning approach uses previously labeled data to facilitate training of a classifier for sleep states, whereas the unsupervised approach aims to discover latent structure and similarities in unlabeled data from which sleep stages are inferred. Read More

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http://dx.doi.org/10.1093/sleep/zsz272DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7215268PMC

MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks.

Sci Rep 2019 10 31;9(1):15793. Epub 2019 Oct 31.

Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan.

Automated sleep stage scoring for mice is in high demand for sleep research, since manual scoring requires considerable human expertise and efforts. The existing automated scoring methods do not provide the scoring accuracy required for practical use. In addition, the performance of such methods has generally been evaluated using rather small-scale datasets, and their robustness against individual differences and noise has not been adequately verified. Read More

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http://dx.doi.org/10.1038/s41598-019-51269-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823352PMC
October 2019

Automatic sleep stage classification based on subcutaneous EEG in patients with epilepsy.

Biomed Eng Online 2019 Oct 30;18(1):106. Epub 2019 Oct 30.

UNEEG medical A/S, Nymoellevej 6, 3540, Lynge, Denmark.

Background: The interplay between sleep structure and seizure probability has previously been studied using electroencephalography (EEG). Combining sleep assessment and detection of epileptic activity in ultralong-term EEG could potentially optimize seizure treatment and sleep quality of patients with epilepsy. However, the current gold standard polysomnography (PSG) limits sleep recording to a few nights. Read More

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http://dx.doi.org/10.1186/s12938-019-0725-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822424PMC
October 2019

On the development of sleep states in the first weeks of life.

PLoS One 2019 29;14(10):e0224521. Epub 2019 Oct 29.

Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria.

Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes correspond to the rapid cortical development at this age. Manual sleep classification is specifically challenging in this population given major body movements and frequent shifts between vigilance states; in addition various staging criteria co-exist. Read More

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0224521PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6818777PMC

EEG sleep stages identification based on weighted undirected complex networks.

Comput Methods Programs Biomed 2020 Feb 9;184:105116. Epub 2019 Oct 9.

Open Access College, University of Southern Queensland, Australia. Electronic address:

Background And Objective: Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks.

Methods: Each EEG segment is partitioned into a number of blocks using a sliding window technique. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.105116DOI Listing
February 2020
1.897 Impact Factor

[Analyzed the related factors of VOTE score for drug-induced sleep endoscopy in patients with obstructive sleep apnea].

Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2019 Oct;33(10):941-944

Department of Otorhinolaryngology,Southern Hospital,Southern Medical University,Guangzhou,510515,China.

To analyze the related factors of VOTE score for drug-induced endoscopy(DISE) in patients with obstructive sleep apnea (OSA). Fifty-four OSA patients, diagnosed by polysomnograph, underwent surgical treatment from Nov 2014 to Dec 2016 in our hospital. All patients underwent drug induced sleep endoscope, and then the collapse of pharyngeal space was evaluated. Read More

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http://dx.doi.org/10.13201/j.issn.1001-1781.2019.10.010DOI Listing
October 2019
4 Reads

Ear-EEG-based sleep scoring in epilepsy: A comparison with scalp-EEG.

J Sleep Res 2019 Oct 17:e12921. Epub 2019 Oct 17.

Neurological Department, Zealand University Hospital, Roskilde, Denmark.

Ear-EEG is a wearable electroencephalogram-recording device. It relies on recording electrodes that are nested within a custom-fitted earpiece in the external ear canal. The concept has previously been tested for seizure detection in epileptic patients and for sleep recordings in a healthy population. Read More

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http://dx.doi.org/10.1111/jsr.12921DOI Listing
October 2019
2 Reads

Automatic Sleep Staging in Patients With Obstructive Sleep Apnea Using Single-Channel Frontal EEG.

J Clin Sleep Med 2019 10;15(10):1411-1420

Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan.

Study Objectives: Reliable sleep staging is difficult to obtain from home sleep testing for diagnosis of obstructive sleep apnea (OSA), especially when it is self-applied. Hence, the current study aimed to develop a single frontal electroencephalography-based automatic sleep staging system (ASSS).

Methods: The ASSS system was developed on a clinical dataset, with a high percentage of participants with OSA. Read More

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http://dx.doi.org/10.5664/jcsm.7964DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778346PMC
October 2019
4 Reads

Sleep in posttraumatic stress disorder: A systematic review and meta-analysis of polysomnographic findings.

Sleep Med Rev 2019 12 26;48:101210. Epub 2019 Aug 26.

Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China. Electronic address:

Polysomnographic studies have been performed to examine sleep abnormalities in posttraumatic stress disorder (PTSD), but clear associations between PTSD and sleep disturbances have not been established. A systematic review of the evidence examining the polysomnographic changes in PTSD patients compared with controls was conducted using MEDLINE, EMBASE, All EBM databases, PsycINFO, and CINAHL databases. Meta-analysis was undertaken where possible. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S10870792193008
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http://dx.doi.org/10.1016/j.smrv.2019.08.004DOI Listing
December 2019
2 Reads

Automated sleep scoring: A review of the latest approaches.

Sleep Med Rev 2019 12 9;48:101204. Epub 2019 Aug 9.

Institute for Information Systems and Networking, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland. Electronic address:

Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a human expert, according to official standards. It could appear then a suitable task for modern artificial intelligence algorithms. Indeed, machine learning algorithms have been applied to sleep scoring for many years. Read More

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http://dx.doi.org/10.1016/j.smrv.2019.07.007DOI Listing
December 2019

Development of a rule-based automatic five-sleep-stage scoring method for rats.

Biomed Eng Online 2019 Sep 4;18(1):92. Epub 2019 Sep 4.

Department of Automatic Control Engineering, Feng Chia University, Taichung, 407, Taiwan.

Background: Sleep problem or disturbance often exists in pain or neurological/psychiatric diseases. However, sleep scoring is a time-consuming tedious labor. Very few studies discuss the 5-stage (wake/NREM1/NREM2/transition sleep/REM) automatic fine analysis of wake-sleep stages in rodent models. Read More

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http://dx.doi.org/10.1186/s12938-019-0712-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6727553PMC
September 2019
1 Read

Dynamic functional connectivity states characterize NREM sleep and wakefulness.

Hum Brain Mapp 2019 12 24;40(18):5256-5268. Epub 2019 Aug 24.

Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.

According to recent neuroimaging studies, temporal fluctuations in functional connectivity patterns can be clustered into dynamic functional connectivity (DFC) states and correspond to fluctuations in vigilance. However, whether there consistently exist DFC states associated with wakefulness and sleep stages and what are the characteristics and electrophysiological origin of these states remain unclear. The aims of the current study were to investigate the properties of DFC in different sleep stages and to explore the relationship between the characteristics of DFC and slow-wave activity. Read More

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http://dx.doi.org/10.1002/hbm.24770DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865216PMC
December 2019
2 Reads

Automatic A-Phase Detection of Cyclic Alternating Patterns in Sleep Using Dynamic Temporal Information.

IEEE Trans Neural Syst Rehabil Eng 2019 09 12;27(9):1695-1703. Epub 2019 Aug 12.

The identification of recurrent, transient perturbations in brain activity during sleep, so called cyclic alternating patterns (CAP), is of significant interest as they have been linked to neurological pathologies. CAP sequences comprise multiple, consecutive cycles of phasic activation (A-phases). Here, we propose a novel, automated system exploiting the dynamical, temporal information in electroencephalography (EEG) recordings for the classification of A-phases and their subtypes. Read More

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http://dx.doi.org/10.1109/TNSRE.2019.2934828DOI Listing
September 2019
1 Read

Liver disease severity is poorly related to the presence of restless leg syndrome in patients with cirrhosis.

Neurol India 2019 May-Jun;67(3):732-737

Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India.

Context: Restless leg syndrome (RLS) has been reported to be common in patients with cirrhosis. The relation of RLS with severity of liver disease is, however, unclear.

Aim: We studied the association between occurrence of RLS and severity of cirrhosis. Read More

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http://dx.doi.org/10.4103/0028-3886.263171DOI Listing
February 2020

Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features.

Front Neuroinform 2019 28;13:45. Epub 2019 Jun 28.

School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Toowoomba, QLD, Australia.

K-complexes identification is a challenging task in sleep research. The detection of k-complexes in electroencephalogram (EEG) signals based on visual inspection is time consuming, prone to errors, and requires well-trained knowledge. Many existing methods for k-complexes detection rely mainly on analyzing EEG signals in time and frequency domains. Read More

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https://www.frontiersin.org/article/10.3389/fninf.2019.00045
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http://dx.doi.org/10.3389/fninf.2019.00045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6609999PMC
June 2019
2 Reads

Benchmark on a large cohort for sleep-wake classification with machine learning techniques.

NPJ Digit Med 2019 7;2:50. Epub 2019 Jun 7.

Qatar Computing Research Institute, HBKU, Doha, Qatar.

Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However, the largest experiments conducted to date, have had only hundreds of participants. Read More

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http://dx.doi.org/10.1038/s41746-019-0126-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555808PMC
June 2019
3 Reads

Development of a human-computer collaborative sleep scoring system for polysomnography recordings.

PLoS One 2019 10;14(7):e0218948. Epub 2019 Jul 10.

Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan.

The overnight polysomnographic (PSG) recordings of patients were scored by an expert to diagnose sleep disorders. Visual sleep scoring is a time-consuming and subjective process. Automatic sleep staging methods can help; however, the mechanism and reliability of these methods are not fully understood. Read More

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218948PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6619661PMC
February 2020
5 Reads

Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.

Sleep 2019 10;42(11)

Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, TN.

Study Objectives: Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. Deep learning is a form of machine learning that uses neural networks to recognize data patterns by inspecting many examples rather than by following explicit programming. Read More

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http://dx.doi.org/10.1093/sleep/zsz159DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6802563PMC
October 2019
3 Reads

Practical aspects of actigraphy and approaches in clinical and research domains.

Handb Clin Neurol 2019 ;160:371-379

Sleep Disorders Center, Neurologic Institute, Cleveland Clinic, Cleveland, OH, United States. Electronic address:

Actigraphy involves acquisition of data using a movement sensor worn continuously on the nondominant wrist, typically for a week or more. Computer-based algorithms estimate sleep episodes by analysis of continuous minutes of no to low movement, or spans of time when movement is relatively low compared with movements during presumed ambulatory wakefulness. Inherent advantages of actigraphy over polysomnography include its noninvasive nature, cost-effectiveness, lesser burden on patients/research participants, and ability to collect data over multiple days/nights, thereby allowing examination of sleep-wake patterning. Read More

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http://dx.doi.org/10.1016/B978-0-444-64032-1.00024-2DOI Listing
December 2019
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Qualitative Scoring of the Pentagon Test: A Tool for the Identification of Subtle Cognitive Deficits in Isolated REM Sleep Behavior Disorder Patients.

Arch Clin Neuropsychol 2019 Oct;34(7):1113-1120

Department of Clinical Neurosciences, Neurology-Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Milan, Italy.

Objective: Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) frequently represents the prodromal stage of alpha-synucleinopathies, and similar to these pathologies, iRBD patients show neuropsychological deficits, particularly in the domain of visuospatial abilities and executive functions. We hypothesized that the qualitative scoring of the Mini-Mental State Examination pentagon test (QSPT) may detect subtle visuospatial deficits in these subjects, and we evaluated its relationship with indexes of sleep quality, as measured by polysomnography.

Methods: A total of 80 polysomnography-confirmed iRBD patients and 40 healthy controls (HCs) were retrospectively recruited. Read More

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http://dx.doi.org/10.1093/arclin/acz024DOI Listing
October 2019
5 Reads

An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model.

J Neurosci Methods 2019 08 19;324:108320. Epub 2019 Jun 19.

Laboratory of Functional Neuroscience and Pathologies (LNFP, EA4559), University Research Center (CURS), CHU AMIENS - SITE SUD, Avenue Laënnec, Salouël 80420, France; Faculty of Medicine, University of Picardie Jules Verne, Amiens 80036, France. Electronic address:

Objective: Sleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of sleep stages is very time-consuming and prone to human errors. In this work, we introduce an efficient approach to improve the accuracy of sleep stage scoring and classification for sleep analysis. Read More

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http://dx.doi.org/10.1016/j.jneumeth.2019.108320DOI Listing
August 2019
5 Reads

Abnormal Thalamic Functional Connectivity During Light Non-Rapid Eye Movement Sleep in Children With Primary Nocturnal Enuresis.

J Am Acad Child Adolesc Psychiatry 2020 May 18;59(5):660-670.e2. Epub 2019 Jun 18.

Shengjing Hospital of China Medical University, Shenyang, China.

Objective: To investigate abnormalities of thalamocortical and intrathalamic functional connectivity (FC) in children with primary nocturnal enuresis (PNE) during light non-rapid eye movement (NREM) sleep using a simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) method.

Method: Polysomnographic and EEG-fMRI data were obtained during sleep from 61 children with PNE (age 10.2 ± 1. Read More

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http://dx.doi.org/10.1016/j.jaac.2019.05.028DOI Listing
May 2020
16 Reads

It twitches without kicking - An association between fragmentary myoclonus and arousal?

Clin Neurophysiol 2019 08 1;130(8):1358-1363. Epub 2019 Jun 1.

Department of Neurology, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria.

Objective: Fragmentary myoclonus (FM) is a polysomnographic motor phenomenon of unknown clinical relevance. This study investigates FM prevalence, gender differences, sleep stage distribution and association with clinical factors using recently introduced advanced FM scoring criteria.

Methods: We analyzed polysomnographic recordings of 178 patients of a mixed sleep-disorder patient cohort. Read More

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http://dx.doi.org/10.1016/j.clinph.2019.05.011DOI Listing
August 2019
11 Reads

A review of automated sleep stage scoring based on physiological signals for the new millennia.

Comput Methods Programs Biomed 2019 Jul 2;176:81-91. Epub 2019 May 2.

Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.

Background And Objective: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.04.032DOI Listing
July 2019
2 Reads