REM sleep behaviour disorder in Parkinson's disease (Review)

Diaconu, S.; Falup‐Pecurariu, Oana; Țînț, Diana; Falup‐Pecurariu, Cristian

doi:10.3892/etm.2021.10244

Cited by 16 publications

(13 citation statements)

References 57 publications

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“…Patients may eventually develop neurodegenerative diseases after a few years or decades, and the risk of occurrence ranges from more than 30% at 5 years to more than 90% at14 years. Idiopathic RBD can be used as a pre-exercise biomarker for PD and about 20% of RBD occurred before PD and about 20% of cases had both Parkinson's disease and RBD and more than 50% of RBD occurred for several years after the clinically manifest of Parkinson's disease (Diaconu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Pharmacological Interventions for REM Sleep Behavior Disorder in Parkinson's Disease: A Systematic Review

Yan

Liu

Huang

et al. 2021

Front. Aging Neurosci.

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To review the therapeutic effects of drugs on REM sleep behavior disorder (RBD) in Parkinson's disease (PD) by searching the MEDLINE/PubMed, Embase, Cochrane, and CBM databases. According to the inclusion and exclusion criteria, studies were included after excluding duplicate data. We evaluated the safety and efficacy of pharmacological intervention to improve RBD in patients with Parkinson's disease (PD-RBD). This systematic review mainly describes the drugs that can be used to treat PD-RBD patients. The results have shown that melatonin can be used as the first-line drug for PD-RBD, and clonazepam provides significant improvement on PD-RBD, androtigotine can be used as an alternative drug. However, further large-scale clinical trial studies are still needed to provide the best guidelines for the pharmacological treatment of PD-RBD.

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Section: Introductionmentioning

confidence: 99%

Pharmacological Interventions for REM Sleep Behavior Disorder in Parkinson's Disease: A Systematic Review

Yan

Liu

Huang

et al. 2021

Front. Aging Neurosci.

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“…REM stages are characterized by an increase in heart rate [ 1 ]. Thus, to distinguish between REM and deep-sleep, k-means clustering was conducted on all heart rate data during the deep-sleep stage ( Figure 4 ).…”

Section: Resultsmentioning

confidence: 99%

“…Parkinson’s disease (PD) is a neurodegenerative disorder [ 1 , 2 ] of which the prevalence among people aged 60 years and older is approximately 1–3% [ 3 , 4 ]. PD risk increases with age [ 5 ], and it affects the patient’s daily activities and quality of life [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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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“…In addition, biosensor-based behavior detection approaches are also commonly applied to identify behavioral disorders in certain types of diseases, such as Parkinson’s disease (PD), Stroke, Paralysis, etc. ( Kang et al, 2010 ; Leclair-Visonneau et al, 2017 ; Alvarez et al, 2018 ; Loopez-Larraz et al, 2018 ; Migliaccio et al, 2020 ; Borzì et al, 2021 ; Diaconu et al, 2021 ). Notably, many recent studies have revealed that behavioral biomarkers might be vital indicators of Alzheimer’s disease (AD) in its initial phases ( de et al, 2014 ; Nardone et al, 2022 ; Staal et al, 2021 ), symptoms like motor behavior alteration occur before profound memory deficits ( Tippett and Sergio, 2006 ; Casper et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

Sun

Sun²,

Wang³

et al. 2022

Front. Bioeng. Biotechnol.

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In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer’s disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.

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REM sleep behaviour disorder in Parkinson's disease (Review)

Cited by 16 publications

References 57 publications

Pharmacological Interventions for REM Sleep Behavior Disorder in Parkinson's Disease: A Systematic Review

Pharmacological Interventions for REM Sleep Behavior Disorder in Parkinson's Disease: A Systematic Review

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

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

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