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

• Published in: Biosensors (Basel) Vol. 12; no. 2; p. 74
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.01.2022; MDPI
• Subjects: Accelerometers; Algorithms; Behavior disorders; Blood pressure; Clonazepam; Clonazepam - pharmacology; Cluster analysis; Clustering; Heart rate; Humans; Learning algorithms; Machine learning; Movement disorders; Neurodegenerative diseases; Parkinson Disease - diagnosis; Parkinson's disease; Patients; Physiology; Quality assessment; Quality control; Quality of life; Questionnaires; REM sleep; REM sleep behavior disorder; REM Sleep Behavior Disorder - complications; REM Sleep Behavior Disorder - diagnosis; Sensors; Sleep; Sleep disorders; smartwatch sensors; Smartwatches; Statistical analysis; Vector quantization; Wearable computers; Taiwan; machine learning; smartwatch sensors; Parkinson’s disease; REM sleep behavior disorder
• ISSN: 2079-6374; 2079-6374
• DOI: 10.3390/bios12020074

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.