An efficient algorithm for R-R intervals series filtering

Spectral analysis of heart rate variability (HRV) constitute a simple and non invasive way to study the autonomic nervous system (ANS) activity. On-line implementation of this technique would allow to follow the evolution of the ANS activity and to track transient events during medical procedures. However, continuous spectral analysis of HRV is not reliable enough due to the difficulty to obtain a noiseless ECG signal during a long period. Indeed, the consequential effects of each ECG signal perturbation on the R-R intervals gives an erroneous evaluation of HRV spectral analysis. In this article, we describe a real time filtering algorithm for R-R intervals series. This filter is able to detect each disturbed area and to replace the erroneous samples with the most probable ones. Therefore, this method allows detecting and replacing up to 90 % of R-R series erroneous samples while keeping the real recording time and without having any effect, beyond measure, on the frequency analysis result.