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Disruptions in the normal rhythmic functioning of the heart, termed asarrhythmia, often result from qualitative changes in the excitation dynamics ofthe organ. The transitions between different types of arrhythmia areaccompanied by alterations in the spatiotemporal pattern of electrical activitythat can be measured by observing the time-intervals between successiveexcitations of different regions of the cardiac tissue. Using biophysicallydetailed models of cardiac activity we show that the distribution of thesetime-intervals exhibit a systematic change in their skewness during suchdynamical transitions. Further, the leading digits of the normalized intervalsappear to fit Benford's law better at these transition points. This raises thepossibility of using these observations to design a clinical indicator foridentifying changes in the nature of arrhythmia. More importantly, our resultsreveal an intriguing relation between the changing skewness of a distributionand its agreement with Benford's law, both of which have been independentlyproposed earlier as indicators of regime shift in dynamical systems.

Using skewness and the first-digit phenomenon to identify dynamical transitions in cardiac models