Evaluation of an automated single-channel sleep staging algorithm

Ying Wang,1 Kenneth A Loparo,1,2 Monica R Kelly,3 Richard F Kaplan1 1General Sleep Corporation, Euclid, OH, 2Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, 3Department of Psychology, University of Arizona, Tucson, AZ, USA Background: We pr...

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Autores principales:	Wang Y, Loparo KA, Kelly MR, Kaplan RF
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2015
Materias:	Psychiatry RC435-571 Neurophysiology and neuropsychology QP351-495
Acceso en línea:	https://doaj.org/article/45d297e8c59b4a99818e11ba2449f2ce
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spelling	oai:doaj.org-article:45d297e8c59b4a99818e11ba2449f2ce2021-12-02T07:27:50ZEvaluation of an automated single-channel sleep staging algorithm1179-1608https://doaj.org/article/45d297e8c59b4a99818e11ba2449f2ce2015-09-01T00:00:00Zhttps://www.dovepress.com/evaluation-of-an-automated-single-channel-sleep-staging-algorithm-peer-reviewed-article-NSShttps://doaj.org/toc/1179-1608Ying Wang,1 Kenneth A Loparo,1,2 Monica R Kelly,3 Richard F Kaplan1 1General Sleep Corporation, Euclid, OH, 2Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, 3Department of Psychology, University of Arizona, Tucson, AZ, USA Background: We previously published the performance evaluation of an automated electroencephalography (EEG)-based single-channel sleep–wake detection algorithm called Z-ALG used by the Zmachine® sleep monitoring system. The objective of this paper is to evaluate the performance of a new algorithm called Z-PLUS, which further differentiates sleep as detected by Z-ALG into Light Sleep, Deep Sleep, and Rapid Eye Movement (REM) Sleep, against laboratory polysomnography (PSG) using a consensus of expert visual scorers. Methods: Single night, in-lab PSG recordings from 99 subjects (52F/47M, 18–60 years, median age 32.7 years), including both normal sleepers and those reporting a variety of sleep complaints consistent with chronic insomnia, sleep apnea, and restless leg syndrome, as well as those taking selective serotonin reuptake inhibitor/serotonin–norepinephrine reuptake inhibitor antidepressant medications, previously evaluated using Z-ALG were re-examined using Z-PLUS. EEG data collected from electrodes placed at the differential-mastoids (A1–A2) were processed by Z-ALG to determine wake and sleep, then those epochs detected as sleep were further processed by Z-PLUS to differentiate into Light Sleep, Deep Sleep, and REM. EEG data were visually scored by multiple certified polysomnographic technologists according to the Rechtschaffen and Kales criterion, and then combined using a majority-voting rule to create a PSG Consensus score file for each of the 99 subjects. Z-PLUS output was compared to the PSG Consensus score files for both epoch-by-epoch (eg, sensitivity, specificity, and kappa) and sleep stage-related statistics (eg, Latency to Deep Sleep, Latency to REM, Total Deep Sleep, and Total REM). Results: Sensitivities of Z-PLUS compared to the PSG Consensus were 0.84 for Light Sleep, 0.74 for Deep Sleep, and 0.72 for REM. Similarly, positive predictive values were 0.85 for Light Sleep, 0.78 for Deep Sleep, and 0.73 for REM. Overall, kappa agreement of 0.72 is indicative of substantial agreement. Conclusion: This study demonstrates that Z-PLUS can automatically assess sleep stage using a single A1–A2 EEG channel when compared to the sleep stage scoring by a consensus of polysomnographic technologists. Our findings suggest that Z-PLUS may be used in conjunction with Z-ALG for single-channel EEG-based sleep staging. Keywords: EEG, sleep staging, algorithm, Zmachine, automatic sleep scoring, sleep detection, single channelWang YLoparo KAKelly MRKaplan RFDove Medical PressarticlePsychiatryRC435-571Neurophysiology and neuropsychologyQP351-495ENNature and Science of Sleep, Vol 2015, Iss default, Pp 101-111 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	Psychiatry RC435-571 Neurophysiology and neuropsychology QP351-495
spellingShingle	Psychiatry RC435-571 Neurophysiology and neuropsychology QP351-495 Wang Y Loparo KA Kelly MR Kaplan RF Evaluation of an automated single-channel sleep staging algorithm
description	Ying Wang,1 Kenneth A Loparo,1,2 Monica R Kelly,3 Richard F Kaplan1 1General Sleep Corporation, Euclid, OH, 2Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, 3Department of Psychology, University of Arizona, Tucson, AZ, USA Background: We previously published the performance evaluation of an automated electroencephalography (EEG)-based single-channel sleep–wake detection algorithm called Z-ALG used by the Zmachine® sleep monitoring system. The objective of this paper is to evaluate the performance of a new algorithm called Z-PLUS, which further differentiates sleep as detected by Z-ALG into Light Sleep, Deep Sleep, and Rapid Eye Movement (REM) Sleep, against laboratory polysomnography (PSG) using a consensus of expert visual scorers. Methods: Single night, in-lab PSG recordings from 99 subjects (52F/47M, 18–60 years, median age 32.7 years), including both normal sleepers and those reporting a variety of sleep complaints consistent with chronic insomnia, sleep apnea, and restless leg syndrome, as well as those taking selective serotonin reuptake inhibitor/serotonin–norepinephrine reuptake inhibitor antidepressant medications, previously evaluated using Z-ALG were re-examined using Z-PLUS. EEG data collected from electrodes placed at the differential-mastoids (A1–A2) were processed by Z-ALG to determine wake and sleep, then those epochs detected as sleep were further processed by Z-PLUS to differentiate into Light Sleep, Deep Sleep, and REM. EEG data were visually scored by multiple certified polysomnographic technologists according to the Rechtschaffen and Kales criterion, and then combined using a majority-voting rule to create a PSG Consensus score file for each of the 99 subjects. Z-PLUS output was compared to the PSG Consensus score files for both epoch-by-epoch (eg, sensitivity, specificity, and kappa) and sleep stage-related statistics (eg, Latency to Deep Sleep, Latency to REM, Total Deep Sleep, and Total REM). Results: Sensitivities of Z-PLUS compared to the PSG Consensus were 0.84 for Light Sleep, 0.74 for Deep Sleep, and 0.72 for REM. Similarly, positive predictive values were 0.85 for Light Sleep, 0.78 for Deep Sleep, and 0.73 for REM. Overall, kappa agreement of 0.72 is indicative of substantial agreement. Conclusion: This study demonstrates that Z-PLUS can automatically assess sleep stage using a single A1–A2 EEG channel when compared to the sleep stage scoring by a consensus of polysomnographic technologists. Our findings suggest that Z-PLUS may be used in conjunction with Z-ALG for single-channel EEG-based sleep staging. Keywords: EEG, sleep staging, algorithm, Zmachine, automatic sleep scoring, sleep detection, single channel
format	article
author	Wang Y Loparo KA Kelly MR Kaplan RF
author_facet	Wang Y Loparo KA Kelly MR Kaplan RF
author_sort	Wang Y
title	Evaluation of an automated single-channel sleep staging algorithm
title_short	Evaluation of an automated single-channel sleep staging algorithm
title_full	Evaluation of an automated single-channel sleep staging algorithm
title_fullStr	Evaluation of an automated single-channel sleep staging algorithm
title_full_unstemmed	Evaluation of an automated single-channel sleep staging algorithm
title_sort	evaluation of an automated single-channel sleep staging algorithm
publisher	Dove Medical Press
publishDate	2015
url	https://doaj.org/article/45d297e8c59b4a99818e11ba2449f2ce
work_keys_str_mv	AT wangy evaluationofanautomatedsinglechannelsleepstagingalgorithm AT loparoka evaluationofanautomatedsinglechannelsleepstagingalgorithm AT kellymr evaluationofanautomatedsinglechannelsleepstagingalgorithm AT kaplanrf evaluationofanautomatedsinglechannelsleepstagingalgorithm
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Evaluation of an automated single-channel sleep staging algorithm

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