Comparison and clinical application of frequency domain methods in analysis of neonatal heart rate time series

The frequency content of the heart rate (HR) series contains information regarding the state of the autonomic nervous system. Of particular importance is respiratory sinus arrhythmia (RSA), the high-frequency fluctuation in HR attributable to respiration. The unevenly sampled nature of heart rate data, however, presents a problem for the discrete Fourier transform. Interpolation of the HR series allows even sampling, but filters high-frequency content. The Lomb periodogram (LP) is a regression-based method that addresses these issues. To evaluate the efficacy of the LP and Fourier techniques in detecting RSA, we compared the spectrum of intervals, the spectrum of HR samples, and the LP of simulated and clinical neonatal time series. We found the LP was superior to the spectrum of intervals and the spectrum of HR samples in analysis near the critical frequency of one half the average sampling rate. Applying the LP to clinical data, we found (1) evidence of stochastic resonance, an enhancement of periodicity with the addition of small amounts of noise, and (2) reduced power at all frequencies prior to clinical diagnosis of neonatal sepsis.