Multiscale analysis of heart rate variability

Abstract

Biological time series are often highly nonlinear and nonstationary. To effectively characterize the complexity of biological signals, we propose a new multiscale analysis method. It has a distinguished feature of scale isolation, and thus can readily deal with nonstationarity in biological signals. By analyzing a number of heart rate variability data, we show that the method can accurately distinguish between healthy subjects and patients with congestive heart failure. Furthermore, our analysis suggests that the dimension of the dynamics of the cardiovascular system is lower under the healthy than under diseased conditions. This is compatible with the observation that a healthy cardiovascular system is a tightly coupled system with coherent functions, while components in a malfunctioning cardiovascular system are somewhat loosely coupled and function incoherently. Therefore, if cardiovascular dynamics could be deterministically chaoslike, it would be more likely to be detected in healthy subjects