Assessing progress of Parkinson's disease using acoustic analysis of phonation

• Authors: MEKYSKA J.; GALAZ Z.; MZOUREK Z.; SMÉKAL Z.; REKTOROVÁ Irena; ELIÁŠOVÁ Ilona; KOŠŤÁLOVÁ Milena; MRAČKOVÁ Martina; BERÁNKOVÁ Dagmar; FAUNDEZ-ZANUY M.; LÓPEZ-DE- IPINA K.; ALONSO-HERNANDEZ Jesus B.
• Year of publication: 2015
• Type: Article in Proceedings
• Conference: 4th International Work Conference on Bio-Inspired Intelligence, IWOBI 2015
• MU Faculty or unit: Central European Institute of Technology
• web: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7160153
• Doi: http://dx.doi.org/10.1109/IWOBI.2015.7160153
• Field: Neurology, neurosurgery, neurosciences
• Keywords: Biodiversity; Conservation; Decision trees; Intelligent systems; Neurodegenerative diseases; Patient rehabilitation; Speech Acoustic analysis
• Description: 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

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