Unsupervised Morphological Segmentation for Detecting Parkinson's Disease

IBANEZ A; OROZCO-ARROYAVE, J; POLOSECKI, P; HESSE E; ROGG,K; EYIGOZ,E; CECCHI, G; SKODDA, S; GARCIA AM

Workshop; AAAI-18 Workshop on Artificial Intelligence Applied to Assistive Technologies and Smart Environments 2018; 2018

The growth of life expectancy entails a rise in prevalence of aging-relatedneurodegenerative disorders, such as Parkinson´s disease. In the ongoing questto find sensitive behavioral markers of this condition, computerized tools prove particularly promising.Here, we propose a novel method utilizing unsupervised morphologicalsegmentation for accessing morphological properties of a speaker´s language.According to our experiments on German, our method can classify patients vs.healthy controls with 81 percent accuracy, and estimate the neurological stateof PD patients with Pearson correlation of 0.46 with respect to the unifiedParkinson?s disease rating scale. Our work is the first study to show thatunsupervised morphological segmentation can be used for automaticdetection of aneurological disorder.