Efficient embedded sleep wake classification for open-source actigraphy

Abstract This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-sour...

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Autores principales:	Tommaso Banfi, Nicolò Valigi, Marco di Galante, Paola d’Ascanio, Gastone Ciuti, Ugo Faraguna
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bc563d4fe26d448399528de23fc2ff5b
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spelling	oai:doaj.org-article:bc563d4fe26d448399528de23fc2ff5b2021-12-02T14:01:35ZEfficient embedded sleep wake classification for open-source actigraphy10.1038/s41598-020-79294-y2045-2322https://doaj.org/article/bc563d4fe26d448399528de23fc2ff5b2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79294-yhttps://doaj.org/toc/2045-2322Abstract This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features’ extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen’s kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.Tommaso BanfiNicolò ValigiMarco di GalantePaola d’AscanioGastone CiutiUgo FaragunaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tommaso Banfi Nicolò Valigi Marco di Galante Paola d’Ascanio Gastone Ciuti Ugo Faraguna Efficient embedded sleep wake classification for open-source actigraphy
description	Abstract This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features’ extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen’s kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.
format	article
author	Tommaso Banfi Nicolò Valigi Marco di Galante Paola d’Ascanio Gastone Ciuti Ugo Faraguna
author_facet	Tommaso Banfi Nicolò Valigi Marco di Galante Paola d’Ascanio Gastone Ciuti Ugo Faraguna
author_sort	Tommaso Banfi
title	Efficient embedded sleep wake classification for open-source actigraphy
title_short	Efficient embedded sleep wake classification for open-source actigraphy
title_full	Efficient embedded sleep wake classification for open-source actigraphy
title_fullStr	Efficient embedded sleep wake classification for open-source actigraphy
title_full_unstemmed	Efficient embedded sleep wake classification for open-source actigraphy
title_sort	efficient embedded sleep wake classification for open-source actigraphy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bc563d4fe26d448399528de23fc2ff5b
work_keys_str_mv	AT tommasobanfi efficientembeddedsleepwakeclassificationforopensourceactigraphy AT nicolovaligi efficientembeddedsleepwakeclassificationforopensourceactigraphy AT marcodigalante efficientembeddedsleepwakeclassificationforopensourceactigraphy AT paoladascanio efficientembeddedsleepwakeclassificationforopensourceactigraphy AT gastoneciuti efficientembeddedsleepwakeclassificationforopensourceactigraphy AT ugofaraguna efficientembeddedsleepwakeclassificationforopensourceactigraphy
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Efficient embedded sleep wake classification for open-source actigraphy

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