Prediction of Parkinson Disease Using Long-term, Short-Term Acoustic Features Based on Machine Learning

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease affecting countless individuals worldwide. PD is characterized by the onset of a marked motor symptomatology in association with several nonmotor manifestations. The clinical phase of the disease is usually preceded by a long prodromal phase, devoid of overt motor symptomatology but often showing some conditions such as sleep disturbance, constipation, anosmia, and phonatory changes. To date, speech analysis appears to be a promising digital biomarker to anticipate even 10 years the onset of clinical PD, as well serving as a useful prognostic tool for patient follow-up. That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS).