Long ECGs reveal rich and robust dynamical regimes in patients with frequent ectopy

The inverse problem for cardiac arrhythmias

A cardiac arrhythmia is an abnormality in the rate or rhythm of the heart beat. We study a type of arrhythmia called a premature ventricular complex (PVC), which is typically benign, but in rare cases can lead to more serious arrhythmias or heart failure. There are three known mechanisms for PVCs: reentry, an ectopic focus, and triggered activity. We develop minimal models for each mechanism and attempt the inverse problem of determining which model (and therefore which mechanism) best describes the beat dynamics observed in an ambulatory electrocardiogram. We demonstrate our approach on a patient who exhibits frequent PVCs and find that their PVC dynamics are best described by a model of triggered activity. Better identification of the PVC mechanism from wearable device data could improve risk stratification for the development of more serious arrhythmias.

Rhythms from Two Competing Periodic Sources Embedded in an Excitable Medium

In an excitable medium, a stimulus generates a wave that propagates in space until it reaches the boundary or collides with another wave and annihilates. We study the dynamics generated by two periodic sources with different frequencies in excitable cardiac tissue culture using optogenetic techniques. The observed rhythms, which can be modeled using cellular automata and studied analytically, show unexpected regularities related to classic results in number theory. We apply the results to identify cardiac arrhythmias in people that are due to a putative mechanism of two competing pacemakers.

Introduction to Focus Issue: Dynamical disease: A translational approach

The concept of Dynamical Diseases provides a framework to understand physiological control systems in pathological states due to their operating in an abnormal range of control parameters: this allows for the possibility of a return to normal condition by a redress of the values of the governing parameters. The analogy with bifurcations in dynamical systems opens the possibility of mathematically modeling clinical conditions and investigating possible parameter changes that lead to avoidance of their pathological states. Since its introduction, this concept has been applied to a number of physiological systems, most notably cardiac, hematological, and neurological. A quarter century after the inaugural meeting on dynamical diseases held in Mont Tremblant, Québec [Bélair et al., Dynamical Diseases: Mathematical Analysis of Human Illness (American Institute of Physics, Woodbury, NY, 1995)], this Focus Issue offers an opportunity to reflect on the evolution of the field in traditional areas as well as contemporary data-based methods.

Using mathematics to diagnose, cure, and predict cardiac arrhythmia

Mathematics can be used to analyze and model cardiac arrhythmia. I discuss three different problems. (1) Diagnosis of atrial fibrillation based on the time intervals between subsequent beats. The probability density histograms of the differences of the intervals between consecutive beats have characteristic shapes for atrial fibrillation. (2) Curing atrial fibrillation by ablation of the core of rotors. Recent clinical studies have proposed that ablating the core of rotors in atrial tissue can cure atrial fibrillation. However, the claims are controversial. One problem that arises relates to difficulties associated with developing algorithms to identify the core of rotors. In model tissue culture systems, heterogeneity in the structure makes it difficult to unambiguously locate the core of rotors. (3) Risk stratification for sudden cardiac death (SCD). Despite numerous clinical studies, there is still a need for improved criteria to assess the risk of SCD. I discuss the possibility of using the dynamics of premature ventricular complexes to help make predictions. The development of wearable devices to record and analyze cardiac rhythms offers new prospects for the diagnosis and treatment of cardiac arrhythmia.