Using chaotic measures to summarize medical signal analysis

Analysis of medical time series such as electrocardiograms (ECG), electroencephalograms (EEG), and other time-dependent data series plays a key role in medical diagnosis. Traditional techniques in signal processing have failed to solve all of the problems associated with data analysis of these nonlinear sets. In previous work, the authors have developed a continuous chaotic approach for the analysis of medical time series that include both graphical and numerical measures. The work described below outlines the use of these measures in higher order decision models such as expert systems, neural networks, and hybrid systems