A new method for change-point detection developed for on-line analysis of the heart beat variability during sleep

We present a novel scaling-dependent measure for times series analysis, the progressive detrended fluctuation analysis (PDFA). Since this method progressively includes and analyzes all data points of the time series, it is suitable for on-line change-point detection: Sudden changes in the statistics of the data points, in the type of correlation or in the statistical variance, or both, are reliably indicated and localized in time. This is first shown for numerous artificially generated data sets of Gaussian random numbers. Also time series with various non-stationarities, such as non-polynomial trends and “spiking”, are included as examples. Although generally applicable, our method was specifically developed as a tool for numerical sleep evaluation based on heart rate variability in the ECG-channel of polysomnographic whole night recordings. It is demonstrated that PDFA can detect specific sleep stage transitions, typically ascending transitions involving sympathetic activation as for example short episodes of wakefulness, and that the method is capable to discern between NREM sleep and REM sleep.