Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear signal processing and statistical machine learning

Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear signal processing and statistical machine learning

Tue, 02/11/2010
13:15 		Athanasios Tsanas (OCIAM and SAMP) Junior Applied Mathematics Seminar Add to calendar Gibson Grd floor SR
Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear signal processing and statistical machine learning
This work demonstrates how we can extract clinically useful patternsextracted 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.