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

TL;DR

This paper demonstrates that analyzing skewness and the first-digit distribution of electrical intervals in cardiac models can serve as early indicators of arrhythmia transitions, potentially aiding clinical diagnosis.

Contribution

It introduces a novel approach using skewness and Benford's law to detect dynamical transitions in cardiac activity models, linking statistical measures to arrhythmia onset.

Findings

Skewness of interval distributions changes systematically during arrhythmia transitions.

Intervals at transition points better fit Benford's law, indicating regime shifts.

The relation between skewness and Benford's law can serve as a diagnostic tool.

Abstract

Disruptions in the normal rhythmic functioning of the heart, termed as arrhythmia, often result from qualitative changes in the excitation dynamics of the organ. The transitions between different types of arrhythmia are accompanied by alterations in the spatiotemporal pattern of electrical activity that can be measured by observing the time-intervals between successive excitations of different regions of the cardiac tissue. Using biophysically detailed models of cardiac activity we show that the distribution of these time-intervals exhibit a systematic change in their skewness during such dynamical transitions. Further, the leading digits of the normalized intervals appear to fit Benford's law better at these transition points. This raises the possibility of using these observations to design a clinical indicator for identifying changes in the nature of arrhythmia. More importantly, our results reveal an intriguing relation between the changing skewness of a distribution and its agreement with Benford's law, both of which have been independently proposed earlier as indicators of regime shift in dynamical systems.