Natural Language Analysis To Detect Parkinson'S Disease

Parkinson's disease (PD) is a neuro-degenerative disorder that produces motor and non-motor impairments. Non-motor impairments include communication and mood disorders. Most of the studies in the literature have been focused on the analysis of motor symptoms. However, non-motor signs are also present in most of the cases. This paper addresses the study of language production as a potential tool to diagnose and monitor the neurological state of PD patients. The study proposes the use of natural language processing methods to extract features from transcriptions obtained from spontaneous speech recordings to discriminate between healthy control people and PD patients. The analysis considered classical features such as Bag of Words and Term Frequency-Inverse Document Frequency, along with methods based on word-embeddings. Accuracies of up to 72% are obtained when discriminating between PD patients and healthy subjects, which confirms that there is information embedded in the language production that can be used for the assessment of the disease.