Predicting the progression of Parkinson's Disease from Speech
Channel: LiNCS
Date: 13/09/2020

Marie-Philippe Gill, Félix Blier, Patrick Cardinal &  Laureano Moro-Velazquez
Présentation réalisée dans le cadre du cours MTI830 (Forage de données textuelles et audio-visuelles) à l'École de technologie supérieure (Université du Québec, Montréal, Canada).
Although the Unified Parkinson’s Disease Rating Scale (UPDRS) is the de-facto gold standard to evaluate the progression of Parkinson’s disease (PD) in patients, it is con- strained to just providing a snapshot for how the patient feels on a given day. In this work, we propose alternative methods to help clinicians and patients gauge this progression and make decisions. Our method relies on using 16 vocal (or speech) features recorded on at-home devices over six months and worn by 42 subjects to predict UPDRS scores. The subjects enrolled were newly diagnosed and not taking any medication. The objective is to understand and analyze the course of PD, through techniques such as observing the central tendency measures, box plots, etc. To study the feasibility of using speech features from sustained vowels, we use a Support Vector Regressor (SVR) and a multilayer perceptron to predict the motor and total UPDRS scores. Preliminary results show that this method of using sustained vowels can provide promising results to assess the severity of PD symptoms. We observe that the condition of 15 subjects improve over the time of the 6-month study, suggesting that the activities performed daily with the device served as therapy. Furthermore, we find that four different groups of patients naturally emerge on clustering the speech features after dimensionality reduction using linear discriminant analysis (LDA). Further analysis could be interesting to obtain insights about the relation between the different groups.