905 results match your criteria Sleep Stage Scoring


Evaluation of cognitive, mental, and sleep patterns of post-acute COVID-19 patients and their correlation with thorax CT.

Acta Neurol Belg 2022 Jun 26. Epub 2022 Jun 26.

Pulmonology Department, Kayseri City Hospital, Kayseri, Turkey.

Objective: In this study, we have evaluated the cognitive, mental, and sleep patterns of post-COVID patients 2 months after their hospitalization, and after scoring their hospitalization thorax CTs, we have compared the degree of the lung involvement with cognitive and mental states of the patients.

Materials And Methods: Forty post-COVID patients were included in our study. Patients who were hospitalized due to COVID-19 and who had thorax CT scan at the admission were included in the study. Read More

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

Comput Biol Med 2022 Jul 21;146:105653. Epub 2022 May 21.

Department of Chest Diseases, Faculty of Medicine, Yuksek Ihtisas University, Ankara, Turkey.

Sleep staging is one of the most important parts of sleep assessment and it has an important role in early diagnosis and intervention of sleep disorders. Manual sleep staging requires a specialist and time which can be affected by subjective factors. So that, automatic sleep-scoring method with high accuracy is beneficial. Read More

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

Medicina (Kaunas) 2022 Jun 9;58(6). Epub 2022 Jun 9.

Honeynaps Research and Development Center, Honeynaps Co., Ltd., 4F, 529, Nonhyeon-ro, Gangnam-gu, Seoul 06126, Korea.

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. Read More

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

Int J Environ Res Public Health 2022 Jun 11;19(12). Epub 2022 Jun 11.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.

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. Read More

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Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal.

Biosensors (Basel) 2022 Jun 17;12(6). Epub 2022 Jun 17.

IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.

In the modern world, wearable smart devices are continuously used to monitor people's health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). Read More

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Automatic sleep scoring with LSTM networks: impact of time granularity and input signals.

Biomed Tech (Berl) 2022 Jun 6. Epub 2022 Jun 6.

University Politehnica of Bucharest, Bucharest, Romania.

Supervised automatic sleep scoring algorithms are usually trained using sleep stage labels manually annotated on 30 s epochs of PSG data. In this study, we investigate the impact of using shorter epochs with various PSG input signals for training and testing a Long Short Term Memory (LSTM) neural network. An LSTM model is evaluated on the provided 30 s epoch sleep stage labels from a publicly available dataset, as well as on 10 s subdivisions. Read More

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Deep Convolutional Recurrent Model for Automatic Scoring Sleep Stages Based on Single-Lead ECG Signal.

Diagnostics (Basel) 2022 May 15;12(5). Epub 2022 May 15.

Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju 26493, Korea.

Background: Sleep stage scoring, which is an essential step in the quantitative analysis of sleep monitoring, relies on human experts and is therefore subjective and time-consuming; thus, an easy and accurate method is needed for the automatic scoring of sleep stages.

Methods: In this study, we constructed a deep convolutional recurrent (DCR) model for the automatic scoring of sleep stages based on a raw single-lead electrocardiogram (ECG). The DCR model uses deep convolutional and recurrent neural networks to apply the complex and cyclic rhythms of human sleep. Read More

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A Holistic Strategy for Classification of Sleep Stages with EEG.

Sensors (Basel) 2022 May 7;22(9). Epub 2022 May 7.

Department of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Korea.

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. Read More

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Performance of Somno-Art Software compared to polysomnography interscorer variability: A multi-center study.

Sleep Med 2022 Aug 27;96:14-19. Epub 2022 Apr 27.

PPRS, Colmar, France. Electronic address:

The visual scoring of gold standard polysomnography (PSG) is known to present inter- and intra-scorer variability. Previously, Somno-Art Software, a cardiac based sleep scoring algorithm, has been validated in comparison to 2 expert visual PSG scorers. The goal of this research is to evaluate the performances of the algorithm against a pool of scorers. Read More

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Automatic sleep stage classification based on two-channel EOG and one-channel EMG.

Physiol Meas 2022 Apr 29. Epub 2022 Apr 29.

China Astronaut Research and Training Center, China Astronaut Research and Training Center, Haidian District, Beijing, China, Beijing, Beijing, 100094, CHINA.

Objective: The sleep monitoring with Polysomnography (PSG) severely degrades the sleep quality. In order to reduce the load of sleep monitoring, an approach to automatic sleep stage classification without electroencephalogram (EEG) was proposed.

Approach: Totally 124 records from the public dataset ISRUC-Sleep with AASM standard were used, in which only 10 records were from the healthy group while the rest ones were from sleep disorder groups. Read More

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SleepSEEG: automatic sleep scoring using intracranial EEG recordings only.

J Neural Eng 2022 05 3;19(2). Epub 2022 May 3.

Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

To perform automatic sleep scoring based only on intracranial electroencephalography (iEEG), without the need for scalp EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction.. Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. Read More

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Associations of Reduced Sympathetic Neural Activity and Elevated Baroreflex Sensitivity With Non-Rapid Eye Movement Sleep: Evidence From Electroencephalogram- and Electrocardiogram-Based Sleep Staging.

Psychosom Med 2022 06 14;84(5):621-631. Epub 2022 Apr 14.

From the Department of Psychiatry (H.-J. Tsai, S.-J. Tsai), Taipei Veterans General Hospital; Institute of Brain Science (H.-J. Tsai, A.C. Yang, S.-J. Tsai, Kuo, C.C.H. Yang), National Yang Ming Chiao Tung University, Digital Medicine Center (A.C. Yang), National Yang Ming Chiao Tung University; Department of Medical Research (A.C. Yang), Taipei Veterans General Hospital, Taipei, Taiwan; Osher Center for Integrative Medicine (Ma), Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School; Center for Dynamical Biomarkers (Ma, Peng), Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts; Clinical Research Center (Kuo), Taoyuan Psychiatric Center, Ministry of Health and Welfare, Taoyuan; and Sleep Research Center (H.-J. Tsai, Kuo, C.C.H. Yang), National Yang Ming Chiao Tung University, Taipei, Taiwan.

Objective: Autonomic neural controls in sleep regulation have been previously demonstrated; however, whether these alternations can be observed by different sleep staging approaches remains unclear. Two established methods for sleep staging-the standardized visual scoring and the cardiopulmonary coupling (CPC) analysis based on electrocardiogram-were used to explore the cardiovascular profiles of sleep.

Methods: Overnight polysomnography was recorded together with continuous beat-to-beat blood pressure. Read More

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Clinical impact of manual scoring of peripheral arterial tonometry in patients with sleep apnea.

Sleep Breath 2022 Apr 2. Epub 2022 Apr 2.

Department of Otorhinolaryngology, Head and Neck Surgery, Kantonsspital Baselland, Liestal, Switzerland.

Purpose: The objective was to analyze the clinical implications of manual scoring of sleep studies using peripheral arterial tonometry (PAT) and to compare the manual and automated scoring algorithms.

Methods: Patients with suspected sleep-disordered breathing underwent sleep studies using PAT. The recordings were analyzed using a validated automated computer-based scoring and a novel manual scoring algorithm. Read More

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Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes.

Biosensors (Basel) 2022 Mar 2;12(3). Epub 2022 Mar 2.

IEN Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Sleep stage classification is an essential process of diagnosing sleep disorders and related diseases. Automatic sleep stage classification using machine learning has been widely studied due to its higher efficiency compared with manual scoring. Typically, a few polysomnography data are selected as input signals, and human experts label the corresponding sleep stages manually. Read More

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Automatic sleep staging of EEG signals: recent development, challenges, and future directions.

Physiol Meas 2022 04 28;43(4). Epub 2022 Apr 28.

Department of Electrical and Computer Engineering, Aarhus University, Denmark.

Modern deep learning holds a great potential to transform clinical studies of human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Read More

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Pain Assessment in Chinese Parkinson's Disease Patients Using King's Parkinson's Disease Pain Scale.

J Pain Res 2022 10;15:715-722. Epub 2022 Mar 10.

Department of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People's Republic of China.

Purpose: To assess Parkinson's disease (PD)-related pain using the Chinese translation of King's Parkinson's disease Pain Scale (KPPS).

Patients And Methods: A cohort of 200 patients with primary PD was recruited for this study. Their demographic and clinical features, including age, disease duration, levodopa equivalent daily dose (LEDD), and scores on the Unified Parkinson's Disease Rating Scale-III (UPDRS III), Hoehn-Yahr Scale (H&Y), Mini-Mental State Examination (MMSE), Activities of Daily Living Scale (ADL), Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA), Pittsburgh Sleep Quality Index (PSQI), Visual Analogue Scale (VAS) and KPPS, were recorded. Read More

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Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.

Sleep Breath 2022 Mar 9. Epub 2022 Mar 9.

Department of Neurology, University of Michigan, Ann Arbor, MI, USA.

Background: The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Read More

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Computer-Assisted Assessment of the Interaction Between Arousals, Breath-by-Breath Ventilation, and Chemical Drive During Cheyne-Stokes Respiration in Heart Failure Patients.

Front Physiol 2022 10;13:815352. Epub 2022 Feb 10.

Laboratory for the Study of Ventilatory Instability, Department of Biomedical Engineering, Montescano Institute - IRCCS, Istituti Clinici Scientifici Maugeri, Montescano, Italy.

Transient increases in ventilation induced by arousal from sleep during Cheyne-Stokes respiration in heart failure patients are thought to contribute to sustaining and exacerbating the ventilatory oscillation. The only possibility to investigate the validity of this notion is to use observational data. This entails some significant challenges: (i) accurate identification of both arousal onset and offset; (ii) detection of short arousals (<3 s); (iii) breath-by-breath analysis of the interaction between arousals and ventilation; (iv) careful control for important confounding factors. Read More

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February 2022

Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification.

J Pers Med 2022 Jan 20;12(2). Epub 2022 Jan 20.

Institute of New Frontier Research, Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon 24252, Korea.

Recently, deep learning for automated sleep stage classification has been introduced with promising results. However, as many challenges impede their routine application, automatic sleep scoring algorithms are not widely used. Typically, polysomnography (PSG) uses multiple channels for higher accuracy; however, the disadvantages include a requirement for a patient to stay one or more nights in the lab wearing uncomfortable sensors and wires. Read More

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January 2022

REM sleep latency changes after version 2.1 of the AASM manual for scoring sleep.

Sleep Med 2022 02 31;90:142-144. Epub 2022 Jan 31.

Neurology Service, Hospital Clínic de Barcelona, Universitat de Barcelona, IDIBAPS, CIBERNED: CB06/05/0018-ISCIII, Barcelona, Spain. Electronic address:

Background: The classical criteria for scoring REM sleep changed in version 2.1 of the AASM manual for scoring sleep, by allowing N1 epochs with atonia precedent and contiguous to definite REM sleep to be scored as REM sleep in the absence of rapid eye movements when the EEG was compatible. This may shorten the REM latency in the Multiple Sleep Latency Test (MSLT) in naps with wake/N1 to REM transitions, characteristic of narcolepsy type 1. Read More

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February 2022

On the role of REM sleep microstructure in suppressing interictal spikes in Electrical Status Epilepticus during Sleep.

Clin Neurophysiol 2022 04 29;136:62-68. Epub 2022 Jan 29.

Child Neuropsychiatry, IRCCS, Giannina Gaslini Institute, Genoa, Italy; Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy. Electronic address:

Objective: Non-Rapid Eye Movement (NREM) sleep promotes the spread and propagation of Interictal Epileptiform Discharges (IEDs), while IEDs are suppressed during REM. Recently, it has been shown that the inhibitory effect on epileptic activity is mostly exerted by the phasic REM (PREM) microstate. This study aims at assessing if this holds true even in the extreme condition of IEDs activation during sleep represented by Electrical Status Epilepticus during Sleep (ESES). Read More

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A scoping review of behavioral sleep stage classification methods for preterm infants.

Sleep Med 2022 02 19;90:74-82. Epub 2022 Jan 19.

Department of Neonatology, University Medical Center Utrecht, Wilhelmina Children's Hospital, Utrecht, the Netherlands; Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands. Electronic address:

Background: Sleep is paramount for optimal brain development in infants admitted to the neonatal intensive care unit. Besides (minimally) invasive technical approaches to study sleep in infants, there is currently a large variety of behavioral sleep stage classification methods (BSSCs) that can be used to identify sleep stages in preterm infants born <37 weeks gestational age. However, they operate different criteria to define sleep stages, which limits the comparability and reproducibility of research on preterm sleep. Read More

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February 2022

A journey toward artificial intelligence-assisted automated sleep scoring.

Authors:
Rui B Chang

Patterns (N Y) 2022 Jan 14;3(1):100429. Epub 2022 Jan 14.

Department of Neuroscience, Department of Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, CT 06520, USA.

Sleep scoring is a tedious, time-consuming process that presents a huge challenge in clinics. Leveraging the state-of-the-art U-net architecture, Zhang et al. developed a deep learning algorithm to simultaneously annotate basic and pathologic sleep stages. Read More

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January 2022

A new tool to screen patients with severe obstructive sleep apnea in the primary care setting: a prospective multicenter study.

BMC Pulm Med 2022 Jan 15;22(1):38. Epub 2022 Jan 15.

Sleep Unit, Department of Respiratory Diseases, Hospital de La Santa Creu I Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.

Background: The coordination between different levels of care is essential for the management of obstructive sleep apnea (OSA). The objective of this multicenter project was to develop a screening model for OSA in the primary care setting.

Methods: Anthropometric data, clinical history, and symptoms of OSA were recorded in randomly selected primary care patients, who also underwent a home sleep apnea test (HSAT). Read More

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January 2022

The Spike-Wave Index of the First 100 Seconds of Sleep Can Be a Reliable Scoring Method for Electrographic Status Epilepticus in Sleep.

J Clin Neurophysiol 2022 Jan 7. Epub 2022 Jan 7.

Nationwide Children's Hospital, Neurology, Columbus, Ohio, U.S.A.; and Rush University Medical College, Chicago, Illinois, U.S.A.

Introduction: Electrical status epilepticus in sleep (ESES) is an electrographic pattern in which interictal epileptiform activity is augmented by the transition to sleep, with non-rapid eye movement sleep state characterized by near-continuous lateralized or bilateral epileptiform discharges. The aim of this study was to measure the reliability of the spike-wave index (SWI) of the first 100 seconds of sleep as a tool for the diagnosis of ESES.

Methods: One hundred forty studies from 60 unique patients met the inclusion. Read More

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January 2022

Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring.

Nat Sci Sleep 2021 24;13:2239-2250. Epub 2021 Dec 24.

Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.

Study Objectives: Automated sleep stage scoring is not yet vigorously used in practice because of the black-box nature and the risk of wrong predictions. The objective of this study was to introduce a confidence-based framework to detect the possibly wrong predictions that would inform clinicians about which epochs would require a manual review and investigate the potential to improve accuracy for automated sleep stage scoring.

Methods: We used 702 polysomnography studies from a local clinical dataset (SNUBH dataset) and 2804 from an open dataset (SHHS dataset) for experiments. Read More

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December 2021

[The accuracy and influencing factors of sleep staging based on single-channel EEG via a deep neural network].

Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2021 Dec;56(12):1256-1262

Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otorhinolaryngology Head and Neck Surgery(Capital Medical University), Ministry of Education, Beijing 100730,China.

To investigate theaccuracy of artificial intelligence sleep staging model in patients with habitual snoring and obstructive sleep apnea hypopnea syndrome (OSAHS) based on single-channel EEG collected from different locations of the head. The clinical data of 114 adults with habitual snoring and OSAHS who visited to the Sleep Medicine Center of Beijing Tongren Hospital from September 2020 to March of 2021 were analyzed retrospectively, including 93 males and 21 females, aging from 20 to 64 years old. Eighty-five adults with OSAHS and 29 subjects with habitual snoring were included. Read More

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December 2021

Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency.

Sensors (Basel) 2021 Dec 8;21(24). Epub 2021 Dec 8.

Department of Computer engineering, Kwangwoon University, Seoul 01897, Korea.

In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. Read More

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December 2021

Neurophysiological Aspects of REM Sleep Behavior Disorder (RBD): A Narrative Review.

Brain Sci 2021 Nov 30;11(12). Epub 2021 Nov 30.

Clinical Neurophysiology Research Unit, Oasi Research Institute-IRCCS, Via Conte Ruggero 73, 94018 Troina, Italy.

REM sleep without atonia (RSWA) is the polysomnographic (PSG) hallmark of rapid eye movement (REM) sleep behavior disorder (RBD), a feature essential for the diagnosis of this condition. Several additional neurophysiological aspects of this complex disorder have also recently been investigated in depth, which constitute the focus of this narrative review, together with RSWA. First, we describe the complex neural network underlying REM sleep and its muscle atonia, focusing on the disordered mechanisms leading to RSWA. Read More

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November 2021

An Efficient Sleep Scoring Method using Visibility Graph and Temporal Features of Single-Channel EEG.

Annu Int Conf IEEE Eng Med Biol Soc 2021 11;2021:6306-6309

This work proposes a method utilizing the fusion of graph-based and temporal features for sleep stage identification. EEG epochs are transformed into visibility graphs from which mean degrees and degree distributions are obtained. In addition, autoregressive model parameters, Higuchi fractal dimension, multi-scale entropy, and Hjorth's parameters are calculated. Read More

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November 2021