Heart rate variability (HRV) is a non-stationary, irregularly sampled signal that represents changes in heart rate over time. The HRV spectrum can be divided into four main ranges covering high, low, very low and ultra-low frequencies. The components lying in these bands, both amplitude and frequency modulated, provide valuable information about various physiological processes. The aim of this study was to verify the usefulness of adaptive variational mode decomposition (AVMD) in the extraction of these components from overnight HRV. The effectiveness of this new approach was compared to multiband filtering (MBF) using a synthetically generated signal, as well as real data from three patients. AVMD turned out to be more robust and effective than MBF, particularly in the high and low frequency ranges, making it a reliable method for deriving the HRV frequency components.Clinical Relevance-The extracted frequency components of heart rate variability provide insight into the regulation of basic physiological processes by the autonomic nervous system.