Parkinson's Disease EMG Signal Prediction Using Neural Networks

This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson's disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns. The experimental results in...

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Main Authors: Zanini, Rafael Anicet, Colombini, Esther Luna, de Castro, Maria Claudia Ferrari
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Correlation
Electromyography
Iron
Neurons
Predictive models
Recurrent neural networks
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creator Zanini, Rafael Anicet
Colombini, Esther Luna
de Castro, Maria Claudia Ferrari
description This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson's disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, and provide reasonable predictions for both EMG envelopes and EMG raw signals. Therefore, one could use these models as input for a control strategy for functional electrical stimulation (FES) devices used on tremor suppression, by dynamically predicting and improving FES control parameters based on tremor forecast.
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subjects Adaptation models
Correlation
Electromyography
Iron
Neurons
Predictive models
Recurrent neural networks
title Parkinson's Disease EMG Signal Prediction Using Neural Networks
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