Analysis of non-linear respiratory influences on Sleep Apnea classification

Alexander Caicedo, Carolina Varon, Sabine Van Huffel

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


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%.

Original languageEnglish (US)
Article number7043112
Pages (from-to)593-596
Number of pages4
JournalComputing in Cardiology
Issue numberJanuary
StatePublished - 2014
Externally publishedYes
Event41st Computing in Cardiology Conference, CinC 2014 - Cambridge, United States
Duration: Sep 7 2014Sep 10 2014

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Cardiology and Cardiovascular Medicine


Dive into the research topics of 'Analysis of non-linear respiratory influences on Sleep Apnea classification'. Together they form a unique fingerprint.

Cite this