In this paper, 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. Studies have shown that many normal physiological systems involve irregularity, while the decrease of irregularity usually implies abnormality. This motivates the use of the statistics for “outliers” in the components 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.

Dong Mao

A New Approach for Physiological Time Series

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