Analysis of non-linear respiratory influences on sleep apnea classification

In this paper we propose the use of Kernel Principal Component Regression (KPCR) in order to model the nonlinear interaction between heart rate (HR) and respiration. We used wavelets in order to decompose the respiratory signal in 2 different frequency bands; namely, the low frequency band (LF) 0-0.078Hz, and the high frequency band (HF) 0.078-2.5Hz. We used the decomposed respiration as regressors in the KPCR model. Using the results provided by KPCR we computed the coupling between HR and the respiration in the LF and HF bands, separately. We evaluated the predictive power of these scores using the Physionet Sleep Apnea Dataset. In addition, we compare these results with the ones previously reported in our group, where we used a linear model based on orthogonal subspace projections and wavelet regression. We found that the features extracted using the nonlinear model improved the classification rate for apneic episodes when compared to the linear model, AUC =92.36 vs AUC = 88.29%.