Sleep quality, REM sleep behavior disorder and daytime sleepiness in adults with and without Parkinson's

Pacheco, Andrea Díaz; Estrella, Jesús Moo

doi:10.31157/archneurosciencesmex.v26i2.271

Cited by 1 publication

(2 citation statements)

References 27 publications

(30 reference statements)

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“…Sleep disturbances were prevalent in PD patients and showed an impact on their quality of life. Of PD patients, 30–37.5% suffered from RBD [ 19 , 39 ]. A suitable method for sleep monitoring, such as wearable smart devices, are proposed as an alternative to PSG because of its cost and inconvenience to patient [ 30 , 40 , 41 ].…”

Section: Discussionmentioning

confidence: 99%

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

Kuo

Wang

et al. 2022

Biosensors

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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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Section: Discussionmentioning

confidence: 99%

“…Similar to the N3 stage, the REM stage is also reduced in patients with PD [ 15 ]. Studies showed that sleep disruption, sleep dysfunction, RBD, and excessive daytime sleepiness are the factors affecting sleep quality and progression of PD [ 15 , 19 ]. Of PD patients, 30–40% were found to have RBD [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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

Kuo

Wang

et al. 2022

Biosensors

View full text Add to dashboard Cite

show abstract

Sleep quality, REM sleep behavior disorder and daytime sleepiness in adults with and without Parkinson's

Cited by 1 publication

References 27 publications

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

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

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