Nonlinear signal processing methods are widely applied in cardiovascular research to characterize heart rate dynamics. This paper investigates the estimation of sympathovagal balance using three different approaches. Six heart rate variability signals obtained from databases were analyzed: three from individuals with normal sinus rhythm and three from patients with congestive heart failure. The methodology evaluates the energy of low-frequency and high-frequency components using both frequency- and time–frequency-domain techniques. Sympathovagal balance was computed for each signal using spectrogram analysis, discrete wavelet transform, and wavelet packet decomposition. After that spectral entropy is also estimated. It measures the distribution of power across frequency bands The results of these methods are presented and compared. Although the clinical relevance of sympathovagal balance remains debated, the experimental findings demonstrate the applicability of the proposed methodology for analyzing heart rate time series. Nonetheless, clinical decisions based on sympathovagal balance should be reserved for specialists.