  We developed a new approach for the analysis of physiological time series. An
iterative convolution filter is used to decompose the time series into various
components. Statistics of these components are extracted as features to
characterize the mechanisms underlying the time series. Motivated by the
studies that show many normal physiological systems involve irregularity while
the decrease of irregularity usually implies the abnormality, the statistics
for "outliers" in the components are used as features measuring irregularity.
Support vector machines are used to select the most relevant features that are
able to differentiate the time series from normal and abnormal systems. This
new approach is successfully used in the study of congestive heart failure by
heart beat interval time series.
