Differential Diagnosis Between Parkinson's Disease and Multiple System Atrophy Using Digital Speech Analysis

Parkinson's disease (PD) is the second most common neurodegenerative disease. Multiple system atrophy (MSA) is a relentlessly progressing rare neurodegenerative disease of unknown etiology. In early stages of the disease, PD and MSA symptoms are very similar, particularly MSA-P where Parkinsonism predominates. The differential diagnosis between MSA-P and PD can be very challenging in early disease stages, while early diagnostic certitude is important for the patient because of the diverging prognosis. Voice disorders are a common early symptom in both diseases and of different origin. The ambition and the originality of this project are to develop a digital voice-based tool for objective discrimination between PD and MSA-P.

Given the clinical similarity between PD and MSA-P in early disease stages and the severity of the prognosis of MSA-P, it would be very useful to have objective tools to assist in the differential diagnosis between both disorders. Since dysarthria is a common early symptom in both diseases and of different origin, the innovative goal of this project is to use dysarthria, through a digital processing of voice recordings of patients, as a vehicle to distinguish between PD and MSA-P in early disease stages. The team will build a corpus of voice samples of patients with both diseases and a recent diagnosis (less than 4 years) and controls. This corpus will consist in sustained vowels, utterances of a standard text and spontaneous speech. The recordings will be performed using a high quality digital recorder (H4n) and the DIANA and EVA-2 workstations. DIANA is a state-of-the-art system dedicated to pathological voice recording and analysis. An electroglottograph (EGG), a non-invasive device, will be also used in conjunction with the recordings to provide the ground truth of glottal opening and closure instants during utterances. The use of an EGG can be very useful given that OGI and GCI provide valuable information about the voice short-time dynamics.