Deep learning for automated sleep staging using instantaneous heart rate

Abstract Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide...

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Autores principales:	Niranjan Sridhar, Ali Shoeb, Philip Stephens, Alaa Kharbouch, David Ben Shimol, Joshua Burkart, Atiyeh Ghoreyshi, Lance Myers
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/c084e2967b6a460f92c88b5f74ed96b3
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spelling	oai:doaj.org-article:c084e2967b6a460f92c88b5f74ed96b32021-12-02T18:51:51ZDeep learning for automated sleep staging using instantaneous heart rate10.1038/s41746-020-0291-x2398-6352https://doaj.org/article/c084e2967b6a460f92c88b5f74ed96b32020-08-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0291-xhttps://doaj.org/toc/2398-6352Abstract Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.Niranjan SridharAli ShoebPhilip StephensAlaa KharbouchDavid Ben ShimolJoshua BurkartAtiyeh GhoreyshiLance MyersNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Niranjan Sridhar Ali Shoeb Philip Stephens Alaa Kharbouch David Ben Shimol Joshua Burkart Atiyeh Ghoreyshi Lance Myers Deep learning for automated sleep staging using instantaneous heart rate
description	Abstract Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.
format	article
author	Niranjan Sridhar Ali Shoeb Philip Stephens Alaa Kharbouch David Ben Shimol Joshua Burkart Atiyeh Ghoreyshi Lance Myers
author_facet	Niranjan Sridhar Ali Shoeb Philip Stephens Alaa Kharbouch David Ben Shimol Joshua Burkart Atiyeh Ghoreyshi Lance Myers
author_sort	Niranjan Sridhar
title	Deep learning for automated sleep staging using instantaneous heart rate
title_short	Deep learning for automated sleep staging using instantaneous heart rate
title_full	Deep learning for automated sleep staging using instantaneous heart rate
title_fullStr	Deep learning for automated sleep staging using instantaneous heart rate
title_full_unstemmed	Deep learning for automated sleep staging using instantaneous heart rate
title_sort	deep learning for automated sleep staging using instantaneous heart rate
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/c084e2967b6a460f92c88b5f74ed96b3
work_keys_str_mv	AT niranjansridhar deeplearningforautomatedsleepstagingusinginstantaneousheartrate AT alishoeb deeplearningforautomatedsleepstagingusinginstantaneousheartrate AT philipstephens deeplearningforautomatedsleepstagingusinginstantaneousheartrate AT alaakharbouch deeplearningforautomatedsleepstagingusinginstantaneousheartrate AT davidbenshimol deeplearningforautomatedsleepstagingusinginstantaneousheartrate AT joshuaburkart deeplearningforautomatedsleepstagingusinginstantaneousheartrate AT atiyehghoreyshi deeplearningforautomatedsleepstagingusinginstantaneousheartrate AT lancemyers deeplearningforautomatedsleepstagingusinginstantaneousheartrate
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Deep learning for automated sleep staging using instantaneous heart rate

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