Parkinson sEMG signal prediction and generation with Neural Networks

Abstract

Parkinson’s Disease (PD) is a neurodegenerative disorder characterized by symptoms like resting and action tremors, which cause severe impairments to the patient’s life. Recently, many assistance techniques have been proposed to minimize the disease’s impact on patients’ life. However, most of these methods depend on data from PD’s surface electromyography (sEMG), which is scarce. In this work, we propose the first methods, based on Neural Networks, for predicting, generating, and transferring the style of patient-specific PD sEMG tremor signals. This dissertation contributes to the area by i) comparing different NN models for predicting PD sEMG signals to anticipate resting tremor patterns ii) proposing the first approach based on Deep Convolutional Generative Adversarial Networks (DCGANs) to generate PD’s sEMG tremor signals; iii) applying Style Transfer (ST) for augmenting PD’s sEMG signals with publicly available datasets of non-PD subjects; iv) proposing metrics for evaluating the PD’s signal characterization in sEMG signals. These new data created by our methods could validate treatment approaches on different movement scenarios, contributing to the development of new techniques for tremor suppression in patients.