Association of periodic limb movements during sleep and Parkinson disease

Hwang, Shang-Rung; Hwang, Sheng-Wei; Chen, Jin-Cherng; Hwang, Juen-Haur

doi:10.1097/md.0000000000018444

Cited by 8 publications

(7 citation statements)

References 26 publications

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“…Sleep periodic limb movement, characterized by repetitive episodes of rigid flexion movements primarily affecting the lower limbs during the first half of the night, represents another significant indicator of sleep disturbance [ 22 ]. Previous studies have reported a higher occurrence of PLM in PD patients, either alone or cooccurring with restless legs syndrome (RLS) [ 23 ]. Covassin et al demonstrated a positive correlation between PLM severity and Unified Parkinson’s Disease Rating Scale (UPDRS) III scores [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

A diagnostic model for Parkinson’s disease based on circadian rhythm-related genes

Wang,

Bao,

Duan

et al. 2024

J Transl Med

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Background Circadian rhythm (CR) disturbance is intricately associated with Parkinson’s disease (PD). However, the involvement of CR-related mechanisms in the pathogenesis and progression of PD remains elusive. Methods A total of 141 PD patients and 113 healthy participants completed CR-related clinical examinations in this study. To further investigate the CR-related mechanisms in PD, we obtained datasets (GSE7621, GSE20141, GSE20292) from the Gene Expression Omnibus database to identify differentially expressed genes between PD patients and healthy controls and further selected CR-related genes (CRRGs). Subsequently, the least absolute shrinkage and selection operator (LASSO) followed by logistic algorithms were employed to identify the hub genes and construct a diagnostic model. The predictive performance was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses in the training set and external validation sets. Finally, RT‒qPCR and Western blotting were conducted to verify the expression of these hub genes in blood samples. In addition, Pearson correlation analysis was utilized to validate the association between expression of hub genes and circadian rhythm function. Results Our clinical observational study revealed that even early-stage PD patients exhibited a higher likelihood of experiencing sleep disturbances, nocturnal hypertension, reverse-dipper blood pressure, and reduced heart rate variability compared to healthy controls. Furthermore, 4 CR-related hub genes (AGTR1, CALR, BRM14, and XPA) were identified and subsequently incorporated as candidate biomarkers to construct a diagnostic model. The model showed satisfactory diagnostic performance in the training set (AUC = 0.941), an external validation set GSE20295 (AUC = 0.842), and our clinical centre set (AUC = 0.805). Additionally, the up-regulation of CALR, BRM14 and the down-regulation of AGTR1, XPA were associated with circadian rhythm disruption. Conclusion CR disturbance seems to occur in the early stage of PD. The diagnostic model based on CR-related genes demonstrated robust diagnostic efficacy, offering novel insights for future clinical diagnosis of PD and providing a foundation for further exploration into the role of CR-related mechanisms in the progression of PD.

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Section: Discussionmentioning

confidence: 99%

A diagnostic model for Parkinson’s disease based on circadian rhythm-related genes

Wang,

Bao,

Duan

et al. 2024

J Transl Med

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“…First, the results have shown the possibility of the automatic detection of the PLMS from only short-term ECG segments. Since the PLMS is automatically detected, it can be used in prescreening for the associated diseases, including the cardiovascular diseases [ 30 ], cerebrovascular risks [ 31 ], and Parkinson disease [ 32 ]. Second, we studied an alternative model for automatic detection of PLMS based on deep learning from the single-lead ECG signal.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals

et al. 2022

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In this study, a deep learning model (deepPLM) is shown to automatically detect periodic limb movement syndrome (PLMS) based on electrocardiogram (ECG) signals. The designed deepPLM model consists of four 1D convolutional layers, two long short-term memory units, and a fully connected layer. The Osteoporotic Fractures in Men sleep (MrOS) study dataset was used to construct the model, including training, validating, and testing the model. A single-lead ECG signal of the polysomnographic recording was used for each of the 52 subjects (26 controls and 26 patients) in the MrOS dataset. The ECG signal was normalized and segmented (10 s duration), and it was divided into a training set (66,560 episodes), a validation set (16,640 episodes), and a test set (20,800 episodes). The performance evaluation of the deepPLM model resulted in an F1-score of 92.0%, a precision score of 90.0%, and a recall score of 93.0% for the control set, and 92.0%, 93.0%, and 90.0%, respectively, for the patient set. The results demonstrate the possibility of automatic PLMS detection in patients by using the deepPLM model based on a single-lead ECG. This could be an alternative method for PLMS screening and a helpful tool for home healthcare services for the elderly population.

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“…PD affects limb movements, particularly lower hand movements, as evidenced by multiple studies [ 5 , 17 - 21 ]. Traditionally, PD diagnosis in the clinical setting relies on neurologists who consider medical history, conduct physical examinations, and observe motor movements and nonmotor symptoms [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study

Parab,

Boster,

Washington

2023

JMIR Form Res

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Background Parkinson disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad or touchscreen interactions, which serve as reliable indicators of PD. Previous works explore keyboard tapping and unstructured device monitoring; we attempt to further these works with structured tests taking into account 2D hand movement in addition to finger tapping. Our feasibility study uses keystroke and mouse movement data from a remotely conducted, structured, web-based test combined with self-reported PD status to create a predictive model for detecting the presence of PD. Objective Analysis of finger tapping speed and accuracy through keyboard input and analysis of 2D hand movement through mouse input allowed differentiation between participants with and without PD. This comparative analysis enables us to establish clear distinctions between the two groups and explore the feasibility of using motor behavior to predict the presence of the disease. Methods Participants were recruited via email by the Hawaii Parkinson Association (HPA) and directed to a web application for the tests. The 2023 HPA symposium was also used as a forum to recruit participants and spread information about our study. The application recorded participant demographics, including age, gender, and race, as well as PD status. We conducted a series of tests to assess finger tapping, using on-screen prompts to request key presses of constant and random keys. Response times, accuracy, and unintended movements resulting in accidental presses were recorded. Participants performed a hand movement test consisting of tracing straight and curved on-screen ribbons using a trackpad or mouse, allowing us to evaluate stability and precision of 2D hand movement. From this tracing, the test collected and stored insights concerning lower arm motor movement. Results Our formative study included 31 participants, 18 without PD and 13 with PD, and analyzed their lower limb movement data collected from keyboards and computer mice. From the data set, we extracted 28 features and evaluated their significances using an extra tree classifier predictor. A random forest model was trained using the 6 most important features identified by the predictor. These selected features provided insights into precision and movement speed derived from keyboard tapping and mouse tracing tests. This final model achieved an average F1-score of 0.7311 (SD 0.1663) and an average accuracy of 0.7429 (SD 0.1400) over 20 runs for predicting the presence of PD. Conclusions This preliminary feasibility study suggests the possibility of using technology-based limb movement data to predict the presence of PD, demonstrating the practicality of implementing this approach in a cost-effective and accessible manner. In addition, this study demonstrates that structured mouse movement tests can be used in combination with finger tapping to detect PD.

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Association of periodic limb movements during sleep and Parkinson disease

Cited by 8 publications

References 26 publications

A diagnostic model for Parkinson’s disease based on circadian rhythm-related genes

A diagnostic model for Parkinson’s disease based on circadian rhythm-related genes

Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals

Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study

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