Diagnosis and Tracking of Parkinson’s Disease by using Automatically Extracted Acoustic Features

Abstract

A system that is capable of automatically discriminating healthy people from people with Parkinson’s Disease (PD) from speech recordings is proposed. It is initially based on 27 features, extracted from recordings of sustained vowels. The number of characteristics has been further reduced by feature selection. The system has been tested by using a heterogeneous database, composed of 40 control subjects and 40 subjects with PD belonging to different severity stages of the disease and under prescribed treatment. Repeated measures per individual were averaged before being assigned to subject, avoiding the usual practice of considering measurements within the same subject as independent. The best overall accuracy obtained was 85.25%, with a sensitivity of 90.23% and a specificity of 80.28%. Additionally, a pilot experiment to track PD severity stages has been performed on 32 out of the 40 initial subjects with PD. To the authors’ knowledge, this is the first speech-based experiment on automatic PD tracking by using the Hoehn and Yahr’s scale (clinical metric mainly focused on postural instability). The results suggest that progression of voice impairment follows different developmental trajectories than postural instability, implying different degenerative mechanisms.