Identification of sleep stages from heart rate variability using a soft-decision wavelet-based technique

This work is concerned with a new technique to find identification factors for the different sleep stages based on a soft-decision wavelet-based estimation of power-spectral density (PSD) contained in the main frequency bands of Heart Rate Variability (HRV). A wavelet-based PSD distribution of HRV in different sleep stages is implemented on an epoch basis. Four sleep stages (S1-S4), REM sleep (with rapid eye movements), and wakefulness are considered in this work. The data used, including electro-cardiograms and sleep stage monitoring hypnograms, are provided by the sleep laboratory of the department of Psychiatry and Psychotherapy of Christian-Albrechts University Kiel, Germany. The data, taken from 12 healthy people and containing enough epochs of the above 5 different sleep stages plus the wake state, is divided into almost equal sets for training and test. The results show that the PSD of the very-low-frequency (VLF) band and the low-frequency (LF) band are reduced as sleep stages vary from the wake state to REM sleep and further to light sleep (S1-S2) and deep sleep (S3-S4). The variation of the PSD in the high-frequency (HF) band is almost the opposite. The ratio of the VLF/HF PSD is found to be a good identification factor between the different sleep stages, showing better results than other, commonly used factors such as the LF/HF and VLF/LF PSD ratios.