The Design of a Parkinson’s Tremor Predictor and Estimator Using a Hybrid Convolutional-Multilayer Perceptron Neural Network

Ibrahim, Abu Bakar; Zhou, Yue; Jenkins, Mary E.; Trejos, Ana Luisa; Naish, Michael D.

doi:10.1109/embc44109.2020.9176132

Cited by 13 publications

(18 citation statements)

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“…As such, in order to effectively suppress tremor with wearable assistive devices and overcome the time delay problem, this work implements a novel approach based on a one-dimensional Convolutional-Multilayer Perceptron model (1D-CNN-MLP) for multistep prediction. The robustness of the 1D-CNN-MLP model for voluntary and tremor time series prediction and estimation was shown in a previous study by the authors [31], where it was demonstrated that the 1D-CNN-MLP model was able to learn correlations between past and present events, and future events. Given a time series, such as tremor signals and voluntary motion, the 1D-CNN-MLP model reads a string of numbers that represent tremor or voluntary motion (e.g., acceleration, velocity, or position) and predicts the number that is most likely to occur next.…”

Section: Introductionmentioning

confidence: 64%

“…A Convolutional Neural Network (CNN) is one type of Neural Network (NN) architecture that takes advantage of the concept of convolutions to learn higher-order features [31], [40], [41]. CNNs are mainly used for image classification and object recognition.…”

Section: One Dimensional Convolutional-multilayer Perceptron and Recurrent Neural Networkmentioning

confidence: 99%

“…The sum of convolutions is passed through the activation function f, followed by a sub-sampling operation before passing the one-dimensional sequences of data to the following layers to learn and extract the features. Herein, the output of the last CNN layer was used in the prediction task that is performed by the Multi-Layer Perceptron (MLP) layer [31] that estimates and predicts voluntary motion. Due to the nonlinear, nonstationary, and stochastic nature of PD tremor, a hybrid of a one-dimensional CNN (1D-CNN) and a MLP network were considered based on the previous work proposed by the authors [31], as shown in Fig.…”

Section: One Dimensional Convolutional-multilayer Perceptron and Recurrent Neural Networkmentioning

confidence: 99%

“…Regardless of the limitations that the RNN-based architectures have, which are their complexity and their slower training process, they have been shown to be robust with time-series data. Moreover, the 1D-CNN-MLP model is a new approach that has been shown to be robust with time-series data [31]. As a result, the five architectures were considered and compared to find the best architecture to be used with WTSDs.…”

Section: One Dimensional Convolutional-multilayer Perceptron and Recurrent Neural Networkmentioning

confidence: 99%

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Real-Time Voluntary Motion Prediction and Parkinson’s Tremor Reduction Using Deep Neural Networks

Ibrahim

Zhou

Jenkins

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Wearable tremor suppression devices (WTSD) have been considered as a viable solution to manage parkinsonian tremor. WTSDs showed their ability to improve the quality of life of individuals suffering from parkinsonian tremor, by helping them to perform activities of daily living (ADL). Since parkinsonian tremor has been shown to be nonstationary, nonlinear, and stochastic in nature, the performance of the tremor models used by WTSDs is affected by their inability to adapt to the nonlinear behaviour of tremor. Another drawback that the models have is their limitation to estimate or predict one step ahead, which introduces delay when used in real time with WTSDs, which compromises performance. To address these issues, this work proposes a deep neural network model that learns the correlations and nonlinearities of tremor and voluntary motion, and is capable of multi-step prediction with minimal delay. A generalized model that is task and user-independent is presented. The model achieved an average estimation percentage accuracy of 99.2%. The average future voluntary motion prediction percentage accuracy with 10, 20, 50, and 100 steps ahead was 97.0%, 94.0%, 91.6%, and 89.9%, respectively, with prediction time as low as 1.5 ms for 100 steps ahead. The proposed model also achieved an average of 93.8% ± 1.5% in tremor reduction when it was tested in an experimental setup in real time. The tremor reduction showed an improvement of 25% over the Weighted Fourier Linear Combiner (WFLC), an estimator commonly used with WTSDs.

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Section: Introductionmentioning

confidence: 64%

Section: One Dimensional Convolutional-multilayer Perceptron and Recurrent Neural Networkmentioning

confidence: 99%

Section: One Dimensional Convolutional-multilayer Perceptron and Recurrent Neural Networkmentioning

confidence: 99%

Section: One Dimensional Convolutional-multilayer Perceptron and Recurrent Neural Networkmentioning

confidence: 99%

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Real-Time Voluntary Motion Prediction and Parkinson’s Tremor Reduction Using Deep Neural Networks

Ibrahim

Zhou

Jenkins

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…However, CNN and transfer learning techniques were not limited to imaging data; they also learn complex features from voices and signal data [ 29 ]. Numerous studies used the biomedical voice ( n = 21) [ 4 , 6 , 22 , 23 , 29 , 33 , 44 , 48 , 50 , 52 , 53 , 55 , 60 , 61 , 73 , 74 , 84 , 93 , 100 , 104 , 105 ] and biometric signal ( n = 14) [ 26 , 31 , 34 , 36 , 45 , 46 , 57 , 62 , 64 , 65 , 68 , 89 , 96 , 98 ]; a few of the included studies used EEG and EMG signals ( n = 5) [ 32 , 39 , 51 , 83 , 85 ].…”

Section: Resultsmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

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Mechatronic Devices for Upper Limb Tremor

Zhou,

Daemi,

Edmonds

et al. 2023

Contemporary Clinical Neuroscience

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The Design of a Parkinson’s Tremor Predictor and Estimator Using a Hybrid Convolutional-Multilayer Perceptron Neural Network

Cited by 13 publications

References 14 publications

Real-Time Voluntary Motion Prediction and Parkinson’s Tremor Reduction Using Deep Neural Networks

Real-Time Voluntary Motion Prediction and Parkinson’s Tremor Reduction Using Deep Neural Networks

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

Mechatronic Devices for Upper Limb Tremor

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