Quantitative assessments of the irregularity or temporal patterning complexity exhibited by biological time series data are emerging as important means for discriminating pathologic from normal in a number of biomedical settings. Endocrine secretory patterns of growth hormone, adrenocorticotrophic hormone, cortisol, luteinizing hormone, testosterone, and others exhibit statistically significant quantifiable differences in the temporal complexity of their expression patterns depending upon health status.

The degree of pattern irregularity of cardiac interbeat intervals of premature infants on the neonatal intensive care unit suggests a possible predictive correlation with the occurrence of neonatal sepsis.

Polyrhythmic or otherwise temporally more complex patterns of circadian body temperature rhythms in an epileptic versus non-epileptic rat model suggest a possible correlation between body temperature regulation and epileptic pathophysiology.

The dynamics of patterns of human interaction may also be discriminable on the basis of quantifiable measures of their temporal patterns of irregularity.

These and other still-emerging examples of complexity analysis applied to biological time series observations offer a rich variety of biomedical phenomena for exploration as an area of immediate practical applied biomathematics that can potentially directly impact human wellness by enhancing clinical diagnostic capabilities.

Faculty associated with this research include:

William S. Evans

Michael L. Johnson

Boris P. Kovatchev

Randall Moorman

Mark Quigg