New chaotic methods for biomedical signal analysis

Biomedical time series provide vital information for both diagnosis of disease and tracking of seriously ill patients. Traditional approaches of analysis are heavily reliant on Fourier analysis. Substantial important information has been derived using this approach but the entire spectrum of biological activities cannot be represented by this method alone. Recent theoretical developments, including wavelet and chaotic analyses, have been shown to be useful in providing additional insight into the behavior of these time series. In the work described, chaotic methods developed by the authors and previously applied to ECG analysis are expanded to include other time series.