Tue, 02 Nov 2010
13:15 -
13:45
Gibson Grd floor SR
Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear signal processing and statistical machine learning
Athanasios Tsanas
(OCIAM and SAMP)
Abstract
This work demonstrates how we can extract clinically useful patterns
extracted
from time series data (speech signals) using nonlinear signal
processing and how to exploit those patterns using robust statistical
machine learning tools, in order to estimate remotely and accurately
average Parkinson's disease symptom severity.