Power spectral analysis of short-term heart rate variability (HRV) can provide instant valuable information to understand the functioning of autonomic control over the cardiovascular system. In this study, an adaptive continuous Morlet wavelet transform (ACMWT) method has been used to describe the time-frequency characteristics of the HRV using band power spectra and the median value of interquartile range. Adaptation of the method was based on the measurement of maximum energy concentration. The ACMWT has been validated on synthetic signals (i.e. stationary, non-stationary as slow varying and fast changing frequency with time) modeled as closest to dynamic changes in HRV signals. This method has been also tested in the presence of additive white Gaussian noise (AWGN) to show its robustness towards the noise. From the results of testing on synthetic signals, the ACMWT was found to be an enhanced energy concentration estimator for assessment of power spectral of short-term HRV time series compared to adaptive Stockwell transform (AST), adaptive modified Stockwell transform (AMST), standard continuous Morlet wavelet transform (CMWT) and Stockwell transform (ST) estimators at statistical significance level of 5%. Further, the ACMWT was applied to real HRV data from Fantasia and MIT-BIH databases, grouped as healthy young group (HYG), healthy elderly group (HEG), arrhythmia controlled medication group (ARCMG), and supraventricular tachycardia group (SVTG) subjects. The global results demonstrate that spectral indices of low frequency power (LFp) and high frequency power (HFp) of HRV were decreased in HEG compared to HYG subjects (p<0.0001). While LFp and HFp indices were increased in ARCMG compared to HEG (p<0.00001). The LFp and HFp components of HRV obtained from SVTG were reduced compared to other group subjects (p<0.00001).


Adaptive continuous Morlet wavelet transform, Energy concentration measurement, Global method, Shape parameter