Parkinson’s Disease Detection based on Changes of Emotions during Speech

Abstract

Parkinson’s disease (PD) is the neurodegenerative disease which affects 2-3 % of the population beyond 65 years of age in EU. When PD treatment is administered early, it is significantly more effective. Unfortunately, it is quite challenging to detect this disease at its early stage and when the symptoms can be recognized it is usually quite late. For this reason there is big motivation for development more accessible and accurate solutions for the detection of PD. One of the early symptoms is so-called hypomimia. This paper introduces an automatic method, which can objectively detect PD. The method is based on analysis of emotion changes during pronunciation defined speech exercises. We achieved balanced accuracy 69 % using XGBoost algorithm. As the exercise we proposed to use a Czech tongue twister - the difficult to pronounce sentence. The features can be explained and thus it can be used in clinical practice. We identified that the most valuable emotion for PD detection in this case is fear.