A machine learning method to process voice samples for identification of Parkinson's disease.

ABSTRACT: Machine learning approaches have been used for the automatic detection of Parkinson's disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring such data. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. This study has two novel contributions. First, we show the reliability of personal telephone-collected voice recordings of the sustained vowel /a/ in natural settings by collecting samples from 50 people with specialist-diagnosed Parkinson's disease and 50 healthy controls and applying machine learning classification with voice features related to phonation. Second, we utilize a novel a

SUBMITTER: Iyer A 

PROVIDER: S-EPMC10667335 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature