EEG for Parkinson’s disease detection

Details

Supervisors

Macq, Benoît

Degree label

Mobilité entrante en sciences de l'ingénieur

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder that mainly affects dopamine-producing neurons in the substantia nigra region of the brain. Unfortunately, the conventional screening methods for PD patients are subjective and manual. Hence, to perform automatic screening of PD patients, objective methods are needed. The electroencephalographic (EEG) data has been used to study the differences in brain signals between PD patients and healthy controls and was further developed an automatic screening tool for Parkinson’s disease, using Machine Learning to individuate its discriminating pattern. To permit an easier access and usage of these algorithms, a web interface was created to ease the access to the results. The purpose of the web interface is to give the possibility to doctors and whoever is interested in the prediction of whether a patient is affected by Parkinson’s or not, to face a familiar and ready to use interface, rather than a laborious Python IDE.