Key result
Stochastic pacing with ARMA modeling accurately predicts cardiac alternans onset in ventricular cell models.
Why the study?
Classical markers based on APD restitution slopes are not always accurate for predicting cardiac alternans, necessitating novel predictive approaches.
Stochastic pacing combined with ARMA model identification represents a novel, accurate approach to predict cardiac alternans using only experimentally measurable quantities like APD and DI.
Alternans of cardiac action potential duration (APD) is a well-known arrhythmogenic mechanism which results from dynamical instabilities. The propensity to alternans is classically investigated by examining APD restitution and by deriving APD restitution slopes as predictive markers. However, experiments have shown that such markers are not always accurate for the prediction of alternans. Using a mathematical ventricular cell model known to exhibit unstable dynamics of both membrane potential and Ca²⁺ cycling, we demonstrate that an accurate marker can be obtained by pacing at cycle lengths (CLs) varying randomly around a basic CL (BCL) and by evaluating the transfer function between the time series of CLs and APDs using an autoregressive-moving-average (ARMA) model. The first pole of this transfer function corresponds to the eigenvalue (λ(alt)) of the dominant eigenmode of the cardiac system, which predicts that alternans occurs when λ(alt) ≤ -1. For different BCLs, control values of λ(alt) were obtained using eigenmode analysis and compared to the first pole of the transfer function estimated using ARMA model fitting in simulations of random pacing protocols. In all versions of the cell model, this pole provided an accurate estimation of λ(alt). Furthermore, during slow ramp decreases of BCL or simulated drug application, this approach predicted the onset of alternans by extrapolating the time course of the estimated λ(alt). In conclusion, stochastic pacing and ARMA model identification represents a novel approach to predict alternans without making any assumptions about its ionic mechanisms. It should therefore be applicable experimentally for any type of myocardial cell.
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Lemay et al. (2012) studied Cardiac action potential duration alternans. Stochastic pacing and ARMA model identification vs. Conventional pacing protocols and eigenmode analysis was evaluated on Estimation of the eigenvalue λ(alt) to predict alternans onset. Stochastic pacing combined with ARMA model identification accurately estimated the dominant eigenvalue λ(alt) and predicted the onset of cardiac alternans in mathematical ventricular cell models.
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