Spectral analysis of heart rate without resampling

Standard methods of estimating the power spectral density (PSD) of irregularly sampled signals such as instantaneous heart rate (HR) require resampling at uniform intervals and replacement of unusable samples. The Lomb periodogram is a means of obtaining PSD estimates directly from irregularly sampled time series, avoiding these requirements. The author compares Fourier, autoregressive, and Lomb PSD estimates from synthetic, real, and noise-corrupted real heart rate time series, and examines systematic differences among these estimates. An algorithm is presented for obtaining a heart rate time series suitable for Lomb PSD estimation from an RR interval time series with included ectopic beats and erroneous measurements. The author concludes with a brief survey of other applications of the technique, such as estimation of respiratory frequency from a time series of beat-by-beat measurements of the mean electrical axis