Analyzing short-term measurements of heart rate variability in the frequency domain using robustly estimated spectral density functions

To assess the variability of heart rate in the frequency domain, usually the spectral density function of the tachogram series is estimated. However, classical spectral density estimates are well known to be prone to outlying observations; hence, robustness is an issue. Therefore, the heart rate variability is assessed by robustly estimating the spectral density function of the tachogram series using a multi-step procedure based on robust filtering. This procedure is insensitive to outliers, and therefore provides fully automated signal processing which will facilitate reliable and reproducible heart rate variability analysis with minimal operator input. Moreover, it can also be used to identify and mark outlying observations. The proposed method is applied to short-term heart rate variability measurements of diabetic patients with different degrees of cardiovascular autonomic neuropathy.