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...
Autores principales: | , , , , , , , , |
---|---|
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 |