Assessing Progress of Parkinson s Disease Using Acoustic Analysis of Phonation

TL;DR

This study uses acoustic analysis of phonation in Czech vowels to estimate Parkinson's disease progression and classify the disease with high accuracy, leveraging speech features and machine learning models.

Contribution

It introduces a novel acoustic parametrization method and machine learning approach for estimating clinical scores and detecting Parkinson's disease from speech.

Findings

Estimated clinical scores with up to 13% error

Achieved 92.86% sensitivity in disease classification

Identified 107 speech features related to hypokinetic dysarthria

Abstract

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.