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

Abstract

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.

The role of wrist-worn technology in the management of Parkinson’s disease in daily life: A narrative review

“…The duration of monitoring in these studies is typically 1 to 2 weeks. However, one study detects abnormal sleep phenomena in PD patients with a right wrist-worn device (ASUS VivoWatch BP) for 2 years ( Ko et al, 2022 ).…”

“…The possible reason is that a single triaxial accelerometer can classify the signals of basic daily movements in a PD patient’s activity, while the uniaxial accelerometer only classifies postural orientations during rest. In addition to movement sensors, photoplethysmography (PPG) sensors are also frequently used in monitoring non-motor and motor signs ( Elzinga et al, 2021 ; Burq et al, 2022 ; Ko et al, 2022 ). PPG is a simple and low-cost wearable device for monitoring blood flow and blood oxygenation.…”

Use of remote monitoring and integrated platform for the evaluation of sleep quality in adult-onset idiopathic cervical dystonia

“…The overall score of baseline standardised questionnaires (PSQI, ESS, DNMSQuest and Beck's Depression Inventory (BDI)) were compared using a t test or Mann-Whitney U statistical approaches. Mean scores of sleep measures from the wrist-worn device and sleep diary were calculated per participant and compared between groups using the Mann-Whitney U test [19][20][21][22][23]. Associations between wrist-worn devices and cases/controls were assessed using generalised additive models, and adjusted for work status, weekly alcohol use and use of sleep medication (obtained from PSQI).…”

A Paradigm Shift in the Management of Patients with Parkinson’s Disease

“…The incorporation of continuous blood pressure measurements into these systems could provide valuable insights into autonomic dysfunction, aiding in the effective management of orthostatic hypotension [74,75]. Advancements in sleep and cognitive disorder technologies are also rapidly progressing [76][77][78], making their integration into future smart systems for comprehensive detection of PD symptoms a feasible possibility.…”