The Time Evolution of Frequency Components of Heart Rate Variability Using Wavelets

The ruling action of the autonomic nervous system (ANS) controls is not static. Due to this dynamic action, the physiological parameters do not remain in the same stationary status but are modified by the evolving condition of the cardiovascular regulatory systems. Applications of conventional spectral approaches, such as Fourier transform (FT) or autoregressive (AR) model, to HRV analysis, thus, always remain problematic since they are applied based on the assumption of stationarity. Also capturing hidden dynamics in both healthy and chronically ill subjects could yield important insights into understanding physiological mechanisms. Heart rate variability analysis is complicated by the fact that these signals are typically both highly irregular and nonstationary i.e., their statistical character changes slowly or intermittently as a result of variations in background influences. FT and AR methods have served a critical role in the understanding of basal autonomic cardiac control. However, these algorithms have limitations in the study of heart rate for nonlinear variations and transient alterations. Here, the wavelet transform based heart rate variability analysis with emphasis on the time evolution of the different frequency components of HRV signal and sympathovagal balance has been shown to monitor ANS adaptations induced by physiological interventions like head up-tilt, forced breathing in the experimental protocol. The procedure was applied to quantify the time evolution of HRV parameters under steady state and to identify subperiods of steady-state during a sequence of physical activities. Results showed the capability of proposed techniques to provide additional practical diagnostic and prognostic insight by mapping autonomic abnormalities.