In this study, we used heart rate variability parameters to first characterize and then automatically detect slow-wave sleep (SWS). First, a wavelet transform was used to decompose equally sampled R-R interval series into their time-dependent spectral components: very low frequency (VLF) 0.005-0-04Hz, low frequency (LF) 0.04-0.15 Hz, and high frequency (HF) 0.15-0.45Hz. Then, the known decrease in LF power during SWS was confirmed and a linear relation between the average LF/HF balance throughout the night and the balance during SWS was found. Also, similar behaviour was found with the VLF power and the VLF/HF ratio. Finally, a decision algorithm with two criteria was defined using a training set of ECG recordings and applied to a test set. The results amounted to an 80% correct identification of SWS. The limitations of the study, as well as inherent differences between SWS definitions based on EEG and ECG, are discussed.
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