Automated restricted Boltzmann machine classifier for early diagnosis of Parkinson’s disease using digitized spiral drawings

Thakur, Mahima; Dhanalakshmi, Samiappan; Kuresan, Harisudha; Senthil, Ramalingam; Narayanamoorthi, R.; Lai, Khin Wee

doi:10.1007/s12652-022-04361-3

Cited by 22 publications

(6 citation statements)

References 35 publications

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“…Deep learning model also plays a good role in mapping nonlinear dynamic systems that are difficult to be expressed by accurate mathematical models. They have been well applied, covering anomaly signal detection [9][10][11], autonomous driving technology [12], surface topography reconstruction [13], image processing [14][15][16], natural language processing [17][18][19], medical image analysis [20][21][22][23], medical diagnosis [24][25][26][27][28][29], and software effort estimates [30]. In terms of dynamic data prediction, Hemavathy and Indumathi [31] proved neural networks can accurately identify the best relay node of underwater acoustic sensor networks combined with dynamic bias tracking algorithms.…”

Section: Related Workmentioning

confidence: 99%

Dynamic modeling and performance evaluation of piezoelectric impact drive system based on neural network

Chen,

Xia,

Song

et al. 2023

Meas. Sci. Technol.

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In metrology and industrial design, the evaluation of measurement uncertainty and error is crucial to the measurement process. The Guide to the Expression of Uncertainty in Measurement and its supplementary documents have established a unified framework and standard for evaluating measurement uncertainty. However, a reasonable method for evaluating dynamic measurement uncertainty has not yet been proposed. By analyzing the dynamic measurement system, and using the long short-term memory time neural network to model the nonlinear dynamics represented by a piezoelectric drive platform, this paper evaluates the system’s dynamic measurement uncertainty through deep integration methods. Bayesian theory is used to propagate probability densities, and experimental results demonstrate the effectiveness of this method for assessing dynamic measurement uncertainty.

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Section: Related Workmentioning

confidence: 99%

Dynamic modeling and performance evaluation of piezoelectric impact drive system based on neural network

Chen,

Xia,

Song

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…Addressing these challenges typically requires iterative experimentation and refinement, utilizing techniques like data augmentation, regularization, and ensemble methods to enhance the robustness and performance of the model. 3,23,28 .…”

Section: Introductionmentioning

confidence: 99%

“…We explore and refine feature selection methodologies and statistical evaluations to improve diagnostic accuracy, differentiating our work from existing approaches. A comparative table in the Materials and Methods section highlights the relative performance of several algorithms, including the ANN classifier, concerning methods previously reported in the literature 3,23,28 . This comparison serves not only to elucidate the effectiveness of the proposed techniques but also to establish a reference point for future investigations in the area.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional methods rely on neurological assessments 27 . Recent research explores multivariate and multimodal data analysis for PD detection using machine learning [28][29][30][31] . This work proposes Multivariate Vocal Data Analysis (MVDA) with machine learning to improve PD detection using speech data 22 .…”

Section: Introductionmentioning

confidence: 99%

“…While existing studies utilize machine learning for PD diagnosis 3,23,28 , the potential of advanced algorithms for combined data analysis remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

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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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Modeling and control of vascular dementia disease by exact dosing of medicines

Vidhyaa,

Vishnu Anirudh,

Panda

et al. 2023

Intl J Robust & Nonlinear

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Alzheimer's and vascular dementia (VD) is the most common neurodegenerative syndromes so the patients lead to loss of memory, a cerebral and behavioral malfunction which may be diagnosed by the presence of two types of neuropathological characteristics such as extracellular plaques consisting of amyloid‐β‐peptides and intracellular neurofibrillary tangles of hyperphosphorylated‐tau‐proteins. Accumulation of these components in the brain needs prediction using models, an alternate tool of experimentation. Mathematical models representing neuroanatomical with pathophysiological characteristics are formulated using continuity balances of bio‐materials to encompass vascular‐cognitive‐impairment. The partial/ordinary differential mathematical model of VD has been simulated in this work and has been used to synthesize control schemes for the prevention of the disease. The non‐linear model has been implemented for prediction‐based automatic control of VD (in virtual mode) with four manipulated physical variables of the patient, concentrations of intracellular amyloid‐beta, tau‐proteins, and microglia; where the virtual controller considers medicinal doses (using virtual nanodosing pumps) to maintain the concentrations of extracellular amyloid‐β‐peptides and neuronal dead‐cells by regulating the oxygen level of the brain within the prescribed level in the clinic. A comparison of performances between the model‐based‐predictive controller (MPC) and PI controllers shows that the former controller is better. Wireless monitoring of amyloid‐beta and tau‐protein from Cerebro‐fluids can be used to design MPC for automatic nanodosing of medicines to prevent dementia. These strategies can be commercially exploited for pharmacological involvement and useful for society to prevent dementia and later Alzheimer's.

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Automated restricted Boltzmann machine classifier for early diagnosis of Parkinson’s disease using digitized spiral drawings

Cited by 22 publications

References 35 publications

Dynamic modeling and performance evaluation of piezoelectric impact drive system based on neural network

Dynamic modeling and performance evaluation of piezoelectric impact drive system based on neural network

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

Modeling and control of vascular dementia disease by exact dosing of medicines

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