New Nonlinear Algorithms for Analysis of Heart Rate Variability: Low-Dimensional Chaos Predicts Lethal Arrhythmias

Abstract

Reduced autonomic control of heartbeat intervals occurs with advanced heart disease and is an independent risk factor for mortality in cardiac patients. Such loss of control is manifested in the heartbeat intervals as a reduction in the total variability, including contributions made by oscillatory reflexes. Animal studies suggest that although the loss of autonomic control may arise following acute coronary artery obstruction, myocardial infarction, or other cardiological events, it may also arise periodically from psychological stress and other transient non-stationary influences mediated by the nervous system. Recent clinical studies of high-risk patients suggest that the deterministic measures of heartbeat dynamics may be more sensitive and specific predictors of risk of death than the more usual stochastic ones, such as the mean, standard deviation or power spectrum. In the present study, several new algorithms based in deterministic chaos theory were applied to a data set made from 20 high-risk patients, each of whom had documented nonsustained ventricular tachycardia (VT) and 10 of whom manifested lethal ventricular fibrillation (VF) within 24 hr. Only the algorithms which measured the time-dependent dimensional complexity (D2i, PD2i) in the data were able to discriminate those patients that later manifested VF. The algorithm which treated the problem of data nonstationarity (PD2i) had the highest sensitivity (100%) and specificity (100%) (P < 0.001, binomial test). Those algorithms which detected order in the data (stochastic-surrogates, determinism, largest Lyapunov exponent, entropy) clearly showed all data to contain low-dimensional chaos, but the order itself did not discriminate between VF and VT risk. It is concluded that among the nonlinear measures of heart rate variability, the ones that quantify the time-dependent complexity, as opposed to detecting the order, are best able to predict clinical risk of sudden cardiac death.