Cepstrum coefficients of the RR series for the detection of obstructive sleep apnea based on different classifiers

Abstract

Two automatic statistical methods for the classification of the obstructive sleep apnoea syndrome based on the cepstrum coefficients of the RR series obtained from the Electrocardiogram (ECG) are presented. We study the effect of working with Linear Discriminant Analysis (LDA) and compare its performance with a reference detector based on Support Vector Machines (SVM). These classifications methods require two previous stages: preprocessing and feature extraction. Firstly, R instants are detected previous to the feature extraction phase thanks to a preprocessing over the ECG. Secondly, Cepstrum Coefficients over the RR signal is applied to extract the relevant characteristics specially those related to the system modelled by the filter-type elements concentrated in the low time lag region.