April 4, 2017
The objective is to detect patients with Parkinson’s Disease (PD) from the voice samples. The training data includes voice measurements such as average, maximum, and minimum vocal fundamental frequency, several measures of variation in fundamental frequency, variation in amplitudes, ratio of noise to tonal components, signal fractal scaling exponent and nonlinear measures of fundamental frequency variation. From these measurements, feature-selection (filter and wrapper method) is performed to select essential features for detecting Parkinson.