Nonlinear dynamic control of irregular cardiac rhythms

Control of voltage-driven instabilities in cardiac myocytes with memory

Sudden cardiac death is known to be associated with dynamical instabilities in the heart, and thus control of dynamical instabilities is considered a potential therapeutic strategy. Different control methods were developed previously, including time-delayed feedback pacing control and constant diastolic interval pacing control. Experimental, theoretical, and simulation studies have examined the efficacy of these control methods in stabilizing action potential dynamics. In this study, we apply these control methods to control complex action potential (AP) dynamics under two diseased conditions: early repolarization syndrome and long QT syndrome, in which voltage-driven instabilities occur in the presence of short-term cardiac memory. In addition, we also develop a feedback pacing method to stabilize these instabilities. We perform theoretical analyses using iterated map models and carry out numerical simulations of AP models. We show that under the normal condition where the memory effect is minimal, all three methods can effectively control the action potential duration (APD) dynamics. Under the two diseased conditions where the memory effect is exacerbated, constant diastolic pacing control is least effective, while the feedback pacing control is most effective. Under a very strong memory effect, all three methods fail to stabilize the voltage-driven instabilities. The failure of effective control is due to memory and the all-or-none AP dynamics which results in very steep changes in APD.

Ontogeny of cardiac sympathetic innervation and its implications for cardiac disease

The vertebrate heart is innervated by the sympathetic and parasympathetic components of the peripheral autonomic nervous system, which regulates its contractile rate and force. Understanding the mechanisms that control sympathetic neuronal growth, differentiation, and innervation of the heart may provide insight into the etiology of cardiac arrhythmogenesis. This review provides an overview of the cell signaling pathways and transcriptional effectors that regulate both the noradrenergic gene program during sympathetic neurogenesis and regional nerve density during cardiac innervation. Recent studies exploring transcriptional regulation of the bHLH transcription factor Hand1 in developing sympathetic neurons are explored, and how the Hand1 sympathetic neuron-specific cis-regulatory element may be used further to assess the contribution of altered sympathetic innervation to human cardiac disease is discussed.

Control of electrical alternans in simulations of paced myocardium using extended time-delay autosynchronization

Experimental studies have linked alternans, an abnormal beat-to-beat alternation of cardiac action potential duration, to the genesis of lethal arrhythmias such as ventricular fibrillation. Prior studies have considered various closed-loop feedback control algorithms for perturbing interstimulus intervals in such a way that alternans is suppressed. However, some experimental cases are restricted in that the controller's stimuli must preempt those of the existing waves that are propagating in the tissue, and therefore only shortening perturbations to the underlying pacing are allowed. We present results demonstrating that a technique known as extended time-delay autosynchronization (ETDAS) can effectively control alternans locally while operating within the above constraints. We show that ETDAS, which has already been used to control chaos in physical systems, has numerous advantages over previously proposed alternans control schemes.

Study of the restitution of action potential duration using the artificial neural network

It is widely accepted that the APD (action potential duration) restitution plays a key role in the initializing and maintaining of the reentry arrhythmias. The Luo-Rudy II models paced with different protocols showed that the current APD had a complex relation with the previous APDs and diastole intervals (DIs). This relation could not be accurately described by a single exponential function. We used an artificial neural network to formularize this relation. The results suggested that back-propagation (BP) network could predict the current APD from the information of the first three previous beats. This would help provide a target for potential anti-arrhythmic therapies.