Assessing Progress of Parkinson's Disease Using Acoustic Analysis of Phonation

This paper deals with a~complex acoustic analysis of phonation in patients with Parkinson's disease (PD) with a~special focus on estimation of disease progress that is described by 7 different clinical scales (e.\,g. Unified Parkinson's disease rating scale or Beck depression inventory). The analysis is based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using classification and regression trees we estimated all clinical scores with maximal error lower or equal to 13\,\%. Best estimation was observed in the case of Mini-mental state examination (MAE = 0.77, estimation error 5.50\,\%). Finally, we proposed a~binary classification based on random forests that is able to identify Parkinson's disease with sensitivity SEN = 92.86\,\% (SPE = 85.71\,\%). The parametrization process was based on extraction of 107 speech features quantifying different clinical signs of hypokinetic dysarthria present in PD.