Heart Rate Spectrum Analysis for the automated Classification of Sleep Stages

Sleep disorders include a huge variety of diseases and their diagnosis very often requires complex and expensive biosignal recordings (polysomnography). Special interest lies in the visual classification of sleep stages in 30 sec windows (epochs) out of this biosignal data, requiring sufficient man-power and experience as well as time. Using the FFT of the heart rate signal to extract the LF/HF frequency power ratio as well as relative peak frequency power within the HF band in combination with the variability of peak frequency power in the HF band we were able to classify sleep stages with an automated algorithm. As signal source we used the ECG signal recorded during the night included in polysomnography recordings from the SIESTA database. The visual 30 sec epoch classification of sleep stages is stored together with the signal data, thereby allowing the comparison of visual classifications to those of the algorithm. While the absolute accuracy of correctly assigned epochs was only 57.5%, our approach can provide a rough overview of the distribution of sleep stages as the clinically relevant result. Therefore, this study represents a first step towards the sleep stage classification from the heart rate signal.