Diagnosing apnea using extreme learning machine with bicoherence features of snore signals

In this study, the performance of a hybrid decision system, Extreme Learning Machine (ELM) accompanied higher order spectra of snoring sounds, in identification of Obstructive Sleep Apnea Syndrome (OSAS), was performed. The method involves characterization of snore sounds through the Quadratic Phase Coupling (QPC) values emerged with the bispectrum analysis of signal. These phase dependent characteristics were basically classified through ELM. By this technique the OSAS and normal snore were identified in a high accuracy. This classification method was considered to help experts in making their final decisions. Keywords: Obstructive sleep apnea syndrome, extreme learning machine, bispectrum analysis, bicoherence;