Detecting Parkinson's Disease Through Speech Analysis

Overview

Speech is an important indicator of motor function and movement coordination and can be extremely sensitive to involvement in the course of neurologic diseases. The aim of this project is to discover for the first time using simple speech recording and high end pattern analysis preclinical stages of disabling central nervous system disorders including Parkinson's disease and other alpha-synucleinopathies in "at high risk" patients with REM sleep behavior disorder and thus provide one essential prerequisite for trials on REM sleep behavior disorder with preventive therapy.

Seven centers of excellence in sleep research will investigate speech and other clinical symptoms in more than 100 subjects with REM sleep behavior disorder. Analyses of a number of unique speech dimensions based upon three fundamental categories of simple speaking tasks will be used to search for specific prodromal alterations in speech patterns in REM sleep behavior disorder, compared to age- and gender-matched patients with early Parkinson's disease and healthy control subjects. Robust algorithms allowing automated speech analysis will be developed and optimized through English, German, French, Czech and Italian languages. Early motor dysfunction strongly predicts Parkinson's disease and other alpha-synucleinopathies. In this regard, vocal assessment has intriguing potential advances as is non-invasive, inexpensive, simple to administer and scalable to large population with possibility to perform recordings remotely, even by telephone from patients' home. Speech analysis may serve as a simple tool to screen large populations for the risk to develop Parkinson's disease. If speech impairment appears to be a strong biomarker of early motor dysfunction, the screening of speech changes may improve stratification for future neuroprotective therapies for Parkinson's disease and other synucleinopathies.