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Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson’s disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiol...

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Autores principales: Ko, Yi-Feng, Kuo, Pei-Hsin, Wang, Ching-Fu, Chen, Yu-Jen, Chuang, Pei-Chi, Li, Shih-Zhang, Chen, Bo-Wei, Yang, Fu-Chi, Lo, Yu-Chun, Yang, Yi, Ro, Shuan-Chu Vina, Jaw, Fu-Shan, Lin, Sheng-Huang, Chen, You-Yin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8869576/
https://www.ncbi.nlm.nih.gov/pubmed/35200335
http://dx.doi.org/10.3390/bios12020074
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author Ko, Yi-Feng
Kuo, Pei-Hsin
Wang, Ching-Fu
Chen, Yu-Jen
Chuang, Pei-Chi
Li, Shih-Zhang
Chen, Bo-Wei
Yang, Fu-Chi
Lo, Yu-Chun
Yang, Yi
Ro, Shuan-Chu Vina
Jaw, Fu-Shan
Lin, Sheng-Huang
Chen, You-Yin
author_facet Ko, Yi-Feng
Kuo, Pei-Hsin
Wang, Ching-Fu
Chen, Yu-Jen
Chuang, Pei-Chi
Li, Shih-Zhang
Chen, Bo-Wei
Yang, Fu-Chi
Lo, Yu-Chun
Yang, Yi
Ro, Shuan-Chu Vina
Jaw, Fu-Shan
Lin, Sheng-Huang
Chen, You-Yin
author_sort Ko, Yi-Feng
collection PubMed
description Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson’s disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient’s sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole–Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.
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spelling pubmed-88695762022-02-25 Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease Ko, Yi-Feng Kuo, Pei-Hsin Wang, Ching-Fu Chen, Yu-Jen Chuang, Pei-Chi Li, Shih-Zhang Chen, Bo-Wei Yang, Fu-Chi Lo, Yu-Chun Yang, Yi Ro, Shuan-Chu Vina Jaw, Fu-Shan Lin, Sheng-Huang Chen, You-Yin Biosensors (Basel) Article Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson’s disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient’s sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole–Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients. MDPI 2022-01-27 /pmc/articles/PMC8869576/ /pubmed/35200335 http://dx.doi.org/10.3390/bios12020074 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
Ko, Yi-Feng
Kuo, Pei-Hsin
Wang, Ching-Fu
Chen, Yu-Jen
Chuang, Pei-Chi
Li, Shih-Zhang
Chen, Bo-Wei
Yang, Fu-Chi
Lo, Yu-Chun
Yang, Yi
Ro, Shuan-Chu Vina
Jaw, Fu-Shan
Lin, Sheng-Huang
Chen, You-Yin
Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease
title Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease
title_full Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease
title_fullStr Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease
title_full_unstemmed Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease
title_short Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson’s Disease
title_sort quantification analysis of sleep based on smartwatch sensors for parkinson’s disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8869576/
https://www.ncbi.nlm.nih.gov/pubmed/35200335
http://dx.doi.org/10.3390/bios12020074
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