Building a Cepstrum-HMM kernel for Apnea identification

Abstract

Authors present an approach based on the transformation of the Cepstral domain on Hidden Markov Model, which is employed for the automatic diagnosis of the Obstructive Sleep Apnea syndrome. The approach includes an Electrocardiogram artefacts removal and R wave detection stage. In addition, the system is modeled by a transformation of the Cepstral domain sequence using Hidden Markov Models (HMM). Final decisions are taken with two different approaches: A Hidden Markov Model and Support Vector Machine classifiers, where the parameterization is based on the transformation of HMM by a kernel. Two public databases have been used for experiments. Firstly, Physionet Apnea-ECG Database for building algorithms, and finally, The St. Vincent's University Hospital/University College Dublin Sleep Apnea Database for testing out with a blind independent dataset. Achieved results were up to 99.23% for Physionet Apnea-ECG Database, and 98.64% for The St. Vincent's Database.