On determining available stochastic features by spectral splitting in obstructive sleep apnea detection

Heart rate variability (HRV) is one of the promising directions for a simple and noninvasive way for obstructive sleep apnea syndrome detection. The time-frequency representations has been proposed before to investigate the non-stationary properties of the HRV during either transient physiological or pathological episodes. Within the framework of the filter-banked feature extraction, estimation of the spectral splitting for stochastic features extraction is an open issue. Usually, this splitting is fixed empirically without taking into account the actual informative distribution of time-frequency representations. In the present work, a relevance-based approach that aims to find a priori a boundaries in the frequency domain for the spectral splitting upon t-f planes is proposed. Results show that the approach is able to find the most informative frequency bands, achieving accuracy rate over 75%.