Cargando…

Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors

Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we exp...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Pin-Wei, O’Brien, Megan K., Horin, Adam P., McGee Koch, Lori L., Lee, Jong Yoon, Xu, Shuai, Zee, Phyllis C., Arora, Vineet M., Jayaraman, Arun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414899/
https://www.ncbi.nlm.nih.gov/pubmed/36015951
http://dx.doi.org/10.3390/s22166190
_version_ 1784776099857694720
author Chen, Pin-Wei
O’Brien, Megan K.
Horin, Adam P.
McGee Koch, Lori L.
Lee, Jong Yoon
Xu, Shuai
Zee, Phyllis C.
Arora, Vineet M.
Jayaraman, Arun
author_facet Chen, Pin-Wei
O’Brien, Megan K.
Horin, Adam P.
McGee Koch, Lori L.
Lee, Jong Yoon
Xu, Shuai
Zee, Phyllis C.
Arora, Vineet M.
Jayaraman, Arun
author_sort Chen, Pin-Wei
collection PubMed
description Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data.
format Online
Article
Text
id pubmed-9414899
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-94148992022-08-27 Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors Chen, Pin-Wei O’Brien, Megan K. Horin, Adam P. McGee Koch, Lori L. Lee, Jong Yoon Xu, Shuai Zee, Phyllis C. Arora, Vineet M. Jayaraman, Arun Sensors (Basel) Article Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data. MDPI 2022-08-18 /pmc/articles/PMC9414899/ /pubmed/36015951 http://dx.doi.org/10.3390/s22166190 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Pin-Wei
O’Brien, Megan K.
Horin, Adam P.
McGee Koch, Lori L.
Lee, Jong Yoon
Xu, Shuai
Zee, Phyllis C.
Arora, Vineet M.
Jayaraman, Arun
Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors
title Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors
title_full Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors
title_fullStr Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors
title_full_unstemmed Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors
title_short Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors
title_sort sleep monitoring during acute stroke rehabilitation: toward automated measurement using multimodal wireless sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414899/
https://www.ncbi.nlm.nih.gov/pubmed/36015951
http://dx.doi.org/10.3390/s22166190
work_keys_str_mv AT chenpinwei sleepmonitoringduringacutestrokerehabilitationtowardautomatedmeasurementusingmultimodalwirelesssensors
AT obrienmegank sleepmonitoringduringacutestrokerehabilitationtowardautomatedmeasurementusingmultimodalwirelesssensors
AT horinadamp sleepmonitoringduringacutestrokerehabilitationtowardautomatedmeasurementusingmultimodalwirelesssensors
AT mcgeekochloril sleepmonitoringduringacutestrokerehabilitationtowardautomatedmeasurementusingmultimodalwirelesssensors
AT leejongyoon sleepmonitoringduringacutestrokerehabilitationtowardautomatedmeasurementusingmultimodalwirelesssensors
AT xushuai sleepmonitoringduringacutestrokerehabilitationtowardautomatedmeasurementusingmultimodalwirelesssensors
AT zeephyllisc sleepmonitoringduringacutestrokerehabilitationtowardautomatedmeasurementusingmultimodalwirelesssensors
AT aroravineetm sleepmonitoringduringacutestrokerehabilitationtowardautomatedmeasurementusingmultimodalwirelesssensors
AT jayaramanarun sleepmonitoringduringacutestrokerehabilitationtowardautomatedmeasurementusingmultimodalwirelesssensors