Advantages of signal-adaptive approaches for the nonlinear, time-variant analysis of heart rate variability of children with temporal lobe epilepsy.

Abstract

Major aim of our study is to demonstrate that signal-adaptive approaches improve the nonlinear and time-variant analysis of heart rate variability (HRV) of children with temporal lobe epilepsy (TLE). Nonlinear HRV analyses are frequently applied in epileptic patients. As HRV is characterized by components with oscillatory properties frequency-selective methods are in the focus, whereby application of nonlinear analysis to linear filtered signals seems to be doubtful. Signal-adaptive methods that preserve nonlinear properties and utilize only the signal data for an automatic computation of the result could benefit to nonlinear analysis of HRV. Combinations of (1) the signal-adaptive Matched Gabor Transform with time-variant nonlinear bispectral analysis and of (2) signal-adaptive Empirical Mode Decomposition methods with time-variant nonlinear stability analysis are investigated with regard to their application in the analysis of specific HRV components (respiratory sinus arrhythmia and Mayer wave associated low-frequency HRV components) of 18 children with TLE. Changes of timing and coordination of both HRV components during preictal, ictal and postictal periods occur which can be better quantified by advanced signal-adaptive methods. Both approaches contribute with specific importance to the analysis.