The variance and the algorithmic complexity of heart rate variability display different responses to anesthesia

In this study, the variance (a distribution-determined measure of disorder) and the algorithmic complexity (a sequence-sensitive measure) were used to characterize heart rate variability before, during and after cardiac surgery. While it is easy to construct mathematical examples where variance and complexity respond differently to parameter changes, this contrasts with the comparative lack of such examples from biological data. The results presented here provide an example, based on clinical data, where the pattern of changes displayed by the complexity is significantly different from the pattern seen in the variance. While complexity recovers to pre-anesthetic levels following cardiac surgery, variance does not. The details of this pattern suggest that of the two measures, complexity may be the more appropriate metric for characterizing changes in the cardiovascular system in response to anesthetics. The findings reported here demonstrate that measurement of complexity can reveal dynamical changes in biological systems not detected by distribution determined measures such as variance.