Selection of Voice Parameters for Diagnosis and Monitoring of Patients with Parkinson's Disease
Abstract: Idiopathic Parkinson's disease (IDP) is the second most common neurodegenerative disorder in the world. Due to the non-existence of a cure, current treatments only focus on improving quality of life by trying to slow the progression of the disease. Therefore, patients require constant monitoring and continuous visits to clinics. Because of this, it is necessary to identify biological markers that allow not only early diagnosis of the disease but also telemonitoring of patients. Different parameters of vocal signals can be used as potential markers for monitoring Parkinson's disease. In this project we present the results of the application of a Machine Learning model for the selection of relevant voice parameters to monitor a patient with IPD. This was done from a database containing 195 voice records from healthy individuals and IPD patients. The classification model that obtained the best result was k-nearest neighbors, with an accuracy of 97.8%. The HNR, RPDE and DFA attributes were also identified as potential voice parameters for patient diagnosis and monitoring.