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

2 November 2010
Athanasios Tsanas
<p>This work demonstrates how we can extract clinically useful patterns</p><p>extracted from time series data (speech signals) using nonlinear signal<br /> processing and how to exploit those patterns using robust statistical<br /> machine learning tools, in order to estimate remotely and accurately<br /> average Parkinson's disease symptom severity.&nbsp;</p> <p>&nbsp;</p>
  • Junior Applied Mathematics Seminar