The Impact of Parkinson’s Disease on General and Specific Motor Aptitudes: A Study of Older Brazilian Adults

Andreis, Lucia Maria; Mariano, Marília; Silva, Lorenna W L; Bianco, Claudia Daniele; Neto, Francisco Rosa

doi:10.1177/00315125211065312

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…However, the absence of mandatory PD notification complicates estimating its prevalence in the country. PD primarily affects the motor system, leading to various motor and non-motor symptoms that can deteriorate patients' quality of life [5][6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

Figueredo,

Monteiro,

Silva

et al. 2024

Preprint

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Parkinson’s disease (PD) is a neurodegenerative disorder caused by a lack of dopamine secretion, resulting in motor dysfunction that affects speech production. The literature indicates that PD can be identified by observing changes in speech signals over time. Early detection of PD is essential to slow its progression and enable patients to access disease-modifying therapies. In this context, an adapted Convolutional Neural Network (CNN) for this task appears promising. In this paper, we propose a CNN classifier to efficiently detect PD with an accuracy above 95\%. We employed advanced machine learning techniques to identify PD early through voice analysis using the Parkinson’s Disease Voice Recording Data Set, which includes the vocal characteristics of 147 PD patients and 48 healthy individuals. The primary objective is to enhance the diagnostic accuracy of PD using a CNN tailored for this task. The preprocessing steps involved cleaning and normalizing the data, followed by the application of Bayesian hyperparameter optimization to improve model performance. The CNN model achieved an average accuracy of 96.8451\% in PD detection, demonstrating high precision, sensitivity, and a balanced F1 score. This result underscores the model’s effectiveness in identifying individuals with PD and highlights the potential of using a single CNN topology, as opposed to more complex hybrid models, to achieve satisfactory diagnostic rates.To evaluate the performance of the proposed approach, we used simulated data generated from the original dataset. The results indicate that the proposed approach is suitable for the automatic identification of PD in practical scenarios.

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Section: Introductionmentioning

confidence: 99%

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

Figueredo,

Monteiro,

Silva

et al. 2024

Preprint

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The activities of daily living partially mediate the relationship between rapid eye movement sleep behavior disorder and depressive symptoms in Parkinson's disease

Wang,

Zheng,

Jing

et al. 2024

Front. Neurol.

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ObjectiveA longitudinal study was conducted to investigate whether rapid eye movement sleep behavior disorder affect depression in patients with Parkinson's disease through activities of daily living.MethodsA total of 387 Parkinson's disease patients' six-year follow-up data (one follow-up per year) were obtained from the Parkinson's Progression Markers Initiative. To allow causal effects to manifest, this study increased the lag period and divided the data from the six follow-ups into two groups: wave 1 (wave refers to time points), wave 3, and wave 5 as one group, and wave 2, wave 4, and wave6 as the other group. The time interval between two time points in each group was two years. To comprehensively and deeply analyze the dynamic relationships between variables, accurately infer causal relationships, control for individual differences, and detect the stability of these relationships, this study constructed the fixed effects cross-lagged panel model (CLPM), the random effects CLPM (RE-CLPM) model, and the Equating CLPM and Equating RE-CLPM models with applied restriction conditions. Additionally, a reverse path was added to verify the reverse prediction effect. The most suitable data analysis model was selected to explore the relationships between the study variables. Furthermore, the longitudinal mediating effect of daily living activities between rapid eye movement sleep behavior disorder and depression was investigated.ResultsIn the models, Equating cross-lagged panel model was the best. The lag effect was positive and significant. In wave 1, 3, 5, activities of daily living mediated 11.82% on the path from rapid eye movement sleep behavior disorder to depression; in wave 2, 4, 6, it mediated 13.13%. Therefore, attention should be paid to the treatment of activities of daily living.ConclusionLongitudinal changes in activities of daily living have indirect effects on the relationship between rapid eye movement sleep behavior disorder and depression, which highlights the importance of changes in activities of daily living ability in Parkinson's disease patients with rapid eye movement sleep behavior disorder.

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The Impact of Parkinson’s Disease on General and Specific Motor Aptitudes: A Study of Older Brazilian Adults

Cited by 2 publications

References 55 publications

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

The activities of daily living partially mediate the relationship between rapid eye movement sleep behavior disorder and depressive symptoms in Parkinson's disease

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