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Automatic scoring of sleep stages and cortical arousals using two electrodes on the forehead: validation in healthy adults

Published on Apr 1, 2014in Journal of Sleep Research3.43
· DOI :10.1111/jsr.12105
Djordje Popovic11
Estimated H-index: 11
(SC: University of Southern California),
Michael C. K. Khoo25
Estimated H-index: 25
(SC: University of Southern California),
Philip R. Westbrook11
Estimated H-index: 11
Cite
Abstract
SUMMARY Accuracy and limitations of automatic scoring of sleep stages and electroencephalogram arousals from a single derivation (Fp1–Fp2) were studied in 29 healthy adults using a portable wireless polysomnographic recorder. All recordings were scored five times: twice by a referent scorer who viewed the standard polysomnographic montage and observed the American Academy of Sleep Medicine rules (referent scoring and blind rescoring); and once by the same scorer who viewed only the Fp1–Fp2 signal (alternative scoring), by another expert from the same institution, and by the algorithm. Automatic, alternative and independent expert scoring were compared with the referent scoring on an epoch-by-epoch basis. The algorithm’s agreement with the reference (81.0%, Cohen’s j = 0.75) was comparable to the inter–rater agreement (83.3%, Cohen’s j = 0.78) or agreement between the referent scoring and manual scoring of the frontopolar derivation (80.7%, Cohen’s j = 0.75). Most misclassifications by the algorithm occurred during uneventful wake/sleep transitions, whereas cortical arousals, rapid eye movement and stable non-rapid eye movement sleep were detected accurately. The algorithm yielded accurate estimates of total sleep time, sleep efficiency, sleep latency, arousal indices and times spent in different stages. The findings affirm the utility of automatic scoring of stages and arousals from a single frontopolar derivation as a method for assessment of sleep architecture in healthy adults.
  • References (19)
  • Citations (11)
Cite
References19
Newest
Published on Apr 1, 2012in Journal of Sleep Research3.43
John R. Shambroom1
Estimated H-index: 1
,
Stephan E. Fábregas1
Estimated H-index: 1
,
Jack Johnstone1
Estimated H-index: 1
SUMMARY The availability of a reliable system to record sleep stage measures easily and automatically in ambulatory settings could be of utility for research and clinical work. The aim of this study was to evaluate a novel wireless system (WS) that does not require skilled preparation for the automatic collection and scoring of human sleep. Twenty-nine healthy adults underwent concurrent sleep measurement via the WS, polysomnography (PSG) and an actigraph (ACT) in a sleep laboratory for one asse...
Published on Jan 1, 2012in International Archives of Medicine
Daniel J. Levendowski14
Estimated H-index: 14
,
Djordje Popovic11
Estimated H-index: 11
+ 1 AuthorsPhilip R. Westbrook11
Estimated H-index: 11
Background Alterations of sleep duration and architecture have been associated with increased morbidity and mortality, and specifically linked to chronic cardiovascular disease and psychiatric disorders, such as type 2 diabetes or depression. Measurement of sleep quality to assist in the diagnosis or treatment of these diseases is not routinely performed due to the complexity and cost of conventional methods. The objective of this study is to cross-validate the accuracy of an automated algorithm...
Published on Jun 1, 2011in Chest9.66
Jennifer L. Martin31
Estimated H-index: 31
,
Alex D. Hakim1
Estimated H-index: 1
Published on Aug 1, 2008 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
Jussi Virkkala19
Estimated H-index: 19
(Finnish Institute of Occupational Health),
Riitta Velin2
Estimated H-index: 2
(Finnish Institute of Occupational Health)
+ 3 AuthorsJoel Hasan18
Estimated H-index: 18
Standard sleep stage classification is based on visual analysis of central EEG, EOG and EMG signals. Automatic analysis with a reduced number of sensors has been studied as an easy alternative to the standard. In this study, a single-channel electro-oculography (EOG) algorithm was developed for separation of wakefulness, SREM, light sleep (S1, S2) and slow wave sleep (S3, S4). The algorithm was developed and tested with 296 subjects. Additional validation was performed on 16 subjects using a low...
Published on Jul 1, 2008in Journal of Neuroscience Methods2.79
Eero Huupponen15
Estimated H-index: 15
,
Kulkas A1
Estimated H-index: 1
+ 3 AuthorsSari-Leena Himanen22
Estimated H-index: 22
Abstract The objective of the present work was to examine fronto-central spindle frequency. A previously validated spindle detector, providing an electroencephalographic (EEG) amplitude independent spindle detection, was used to detect bilateral sleep spindles from sleep EEG recordings of ten healthy subjects with a time resolution of 0.33-s. A bilateral spindle detected centrally and frontopolarly simultaneously is called here a diffuse spindle. A bilateral spindle detected only frontopolarly o...
Published on Nov 1, 2007in Sleep
Christian Berthomier6
Estimated H-index: 6
,
Xavier Drouot10
Estimated H-index: 10
(University of Paris)
+ 6 AuthorsMarie-Pia d'Ortho8
Estimated H-index: 8
(University of Paris)
Study Objective: To assess the performance of automatic sleep scoring software (ASEEGA) based on a single EEG channel comparatively with manual scoring (2 experts) of conventional full polysomnograms. Design: Polysomnograms from 15 healthy individuals were scored by 2 independent experts using conventional R&K rules. The results were compared to those of ASEEGA scoring on an epoch-by-epoch basis. Setting: Sleep laboratory in the physiology department of a teaching hospital. Participants: Fifteen...
Published on Nov 1, 2007in Sleep
Vladimir Svetnik12
Estimated H-index: 12
(MSD: Merck & Co.),
Junshui Ma10
Estimated H-index: 10
(MSD: Merck & Co.)
+ 4 AuthorsKen S. Koblan4
Estimated H-index: 4
(USMA: United States Military Academy)
Objective: To evaluate the performance of 2 automated systems, Morpheus and Somnolyzer24X7, with various levels of human review/editing, in scoring polysomnographic (PSG) recordings from a clinical trial using zolpidem in a model of transient insomnia. Methods: 164 all-night PSG recordings from 82 subjects collected during 2 nights of sleep, one under placebo and one under zolpidem (10 mg) treatment were used. For each recording, 6 different methods were used to provide sleep stage scores based ...
Published on Oct 1, 2007in Journal of Neuroscience Methods2.79
Jussi Virkkala19
Estimated H-index: 19
(Finnish Institute of Occupational Health),
Joel Hasan18
Estimated H-index: 18
+ 2 AuthorsKiti Müller20
Estimated H-index: 20
(Finnish Institute of Occupational Health)
Abstract An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in the 0.5–6 Hz band between the two EOG channels. An automatic slow eye-movement (SEM) estimatio...
Published on Jan 1, 2005in Neuropsychobiology1.68
Peter Anderer50
Estimated H-index: 50
(Medical University of Vienna),
Georg Gruber28
Estimated H-index: 28
(Medical University of Vienna)
+ 15 AuthorsHeidi Danker-Hopfe29
Estimated H-index: 29
To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts h
Published on Oct 1, 2004in Sleep
Stephen D. Pittman12
Estimated H-index: 12
(Brigham and Women's Hospital),
Mary MacDonald7
Estimated H-index: 7
(Brigham and Women's Hospital)
+ 5 AuthorsDavid P. White82
Estimated H-index: 82
(Harvard University)
STUDY OBJECTIVES: To assess the accuracy of an automated system (Morpheus I Sleep Scoring System) for analyzing and quantifying polysomnographic data from a population with sleep-disordered breathing. SETTING: Sleep laboratory affiliated with a tertiary care academic medical center. MEASUREMENTS AND RESULTS: 31 diagnostic polysomnograms were blindly analyzed prospectively with the investigational automated system and manually by 2 registered polysomnography technologists (M1 & M2) from the same ...
Cited By11
Newest
Published on Jul 15, 2019in Nervenarzt0.83
Jochen Klucken37
Estimated H-index: 37
,
Till Gladow + 3 AuthorsBjörn M. Eskofier10
Estimated H-index: 10
(FAU: University of Erlangen-Nuremberg)
Aus dem Fitness- und Lifestylebereich dringen die tragbaren Sensoren – die sog. „wearables“ – zunehmend in die medizinische Versorgungs- und Studienlandschaft. Unterschiedliche Funktionsstorungen im Bereich der neurologischen Erkrankungen eignen sich dabei trefflich, um mithilfe von Wearables patientenzentrierte Parameter und Informationen zu erheben, die zur Pravention, Pradiktion, Diagnostik- und Therapieunterstutzung genutzt werden konnen. Fur eine Anwendung im medizinischen Kontext sind jedo...
Published in Sleep Medicine Reviews10.52
Luigi Fiorillo (SUPSI), Alessandro Puiatti7
Estimated H-index: 7
(SUPSI)
+ -3 AuthorsFrancesca Dalia Faraci4
Estimated H-index: 4
(SUPSI)
Summary 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. As a result, several software products offer nowadays automated or semi-automated scoring services. However, the vast majority of the sleep physicians do not use them. Very recently, th...
Published on Apr 1, 2019in Journal of Sleep Research3.43
Kaare B. Mikkelsen5
Estimated H-index: 5
(University of Oxford),
James K. Ebajemito2
Estimated H-index: 2
(University of Surrey)
+ 8 AuthorsDerk-Jan Dijk90
Estimated H-index: 90
(University of Surrey)
Published on Apr 1, 2019in Sleep Medicine3.36
Amélie Rochefort2
Estimated H-index: 2
(Laval University),
Denise C. Jarrin10
Estimated H-index: 10
(Laval University)
+ 2 AuthorsCharles M. Morin77
Estimated H-index: 77
(Laval University)
Abstract Objectives To examine the potential moderating effect of objectively measured sleep duration at baseline on the response to cognitive behavioral therapy for insomnia (CBT-I), administered singly or combined with medication (CBT-I + Med). Methods Based on the average PSG-derived sleep duration across two baseline nights and the type of treatment received, 159 adults with insomnia (50.3 ± 10.1 years; 61.0% women) were classified into one of four groups: participants with short sleep durat...
Published on Mar 13, 2019in bioRxiv
Christian Berthomier6
Estimated H-index: 6
,
Vincenzo Muto10
Estimated H-index: 10
(University of Liège)
+ 14 AuthorsChristophe Phillips49
Estimated H-index: 49
(University of Liège)
Study Objectives: New challenges in sleep science require to describe fine grain phenomena or to deal with large datasets. Beside the human resource challenge of scoring huge datasets, the inter- and intra-expert variability may also reduce the sensitivity of such studies. Searching for a way to disentangle the variability induced by the scoring method from the actual variability in the data, visual and automatic sleep scorings of healthy individuals were examined. Methods: A first dataset (DS1,...
Published on Mar 15, 2019in arXiv: Neurons and Cognition
Andreas Brink-Kjær (DTU: Technical University of Denmark), Alexander Neergaard Olesen2
Estimated H-index: 2
+ 4 AuthorsHelge Bjarup Dissing Sørensen19
Estimated H-index: 19
Cortical arousals are transient events of disturbed sleep that occur spontaneously or in response to stimuli such as apneic events. The gold standard for arousal detection in human polysomnographic recordings (PSGs) is manual annotation by expert human scorers, a method with significant interscorer variability. In this study, we developed an automated method, the Multimodal Arousal Detector (MAD), to detect arousals using deep learning methods. The MAD was trained on 2,889 PSGs to detect both co...
Published on Jan 1, 2019in NeuroImage5.81
Şükrü Barış Demiral4
Estimated H-index: 4
(WRAIR: Walter Reed Army Institute of Research),
Dardo Tomasi51
Estimated H-index: 51
(NIH: National Institutes of Health)
+ 15 AuthorsClara Freeman3
Estimated H-index: 3
(NIH: National Institutes of Health)
Abstract The role of sleep in brain physiology is poorly understood. Recently rodent studies have shown that the glymphatic system clears waste products from brain more efficiently during sleep compared to wakefulness due to the expansion of the interstitial fluid space facilitating entry of cerebrospinal fluid (CSF) into the brain. Here, we studied water diffusivity in the brain during sleep and awake conditions, hypothesizing that an increase in water diffusivity during sleep would occur conco...
Hao Dong8
Estimated H-index: 8
(Imperial College London),
Akara Supratak4
Estimated H-index: 4
(Imperial College London)
+ 3 AuthorsYike Guo32
Estimated H-index: 32
(Imperial College London)
This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages at lower cost in the home. However, thes...
Published on Jan 1, 2018in IEEE Reviews in Biomedical Engineering
Sirinthip Roomkham1
Estimated H-index: 1
(QUT: Queensland University of Technology),
David Lovell15
Estimated H-index: 15
(QUT: Queensland University of Technology)
+ 1 AuthorsDimitri Perrin9
Estimated H-index: 9
(QUT: Queensland University of Technology)
The market for smartphones, smartwatches, and wearable devices is booming. In recent years, individuals and researchers have used these devices as additional tools to monitor and track sleep, physical activity, and behavior. Their use in sleep research and clinical applications could address the difficulties in scaling up studies that rely on polysomnography, the gold-standard. However, the use of commercial devices for large-scale sleep studies is not without challenges. With this in mind, this...
Published on Aug 6, 2017 in ICML (International Conference on Machine Learning)
Mingmin Zhao7
Estimated H-index: 7
(MIT: Massachusetts Institute of Technology),
Shichao Yue3
Estimated H-index: 3
(MIT: Massachusetts Institute of Technology)
+ 2 AuthorsMatt T. Bianchi18
Estimated H-index: 18
(Harvard University)
We focus on predicting sleep stages from radio measurements without any attached sensors on subjects. We introduce a new predictive model that combines convolutional and recurrent neural networks to extract sleep-specific subject-invariant features from RF signals and capture the temporal progression of sleep. A key innovation underlying our approach is a modified adversarial training regime that discards extraneous information specific to individuals or measurement conditions, while retaining a...