OCIAM and SAMP

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.