Wavelet decomposition of nonstationary heart rate variability

Power spectral density (PSD) of the long-term, 24-hour heart rate variability (LHRV) is widely used for risk stratification and noninvasive assessment of autonomic balance. The method can correctly identify the total power changes, but spectral estimates over narrow frequency ranges are inaccurate because of nonstationary character of the signal. An energy-frequency distribution (EFD) of LHRV based on a wavelet transform is proposed to overcome the nonstationarity problem and to obtain accurate spectral estimates over narrow frequency ranges. Holter ECGs from 54 patients with chronic autonomic imbalance and 27 normal subjects were used to derive the LHRV-signals. Mean EFD showed a characteristic peak that was shifted toward lower frequencies in patients compared to normal subjects. The slope of EFD at the high frequency ranges (>0.0329 Hz) was smaller in patients than in healthy subjects. EFD of 24-hour heart rate variability might be used as a more accurate tool for spectral estimation of the nonstationary frequency components than the power spectral integration over empirical frequency ranges