An Intelligent Algorithm for Home Sleep Apnoea Test Device

This chapter describes the application of an intelligent machine learning technique (Support Vector Machines, SVM) to diagnose the patients with sleep apnoea syndrome using Electrocardiogram (ECG) signal. Sleep apnoea syndrome is a medical condition caused by sleep apnoea which is defined as the cessation of breathing for short periods during sleep. First, the importance of early diagnosis and treatment of sleep apnoea syndrome are presented. This is followed by an introduction to the design of a home diagnostic model for predicating sleep apnoea syndrome from electrocardiogram recordings. Examples are presented using SVM to build a reliable model that utilizes key indices of physiological measurements (ECG signals). A number of recommendations have been proposed for assessing a classifier model in recognizing patients with sleep apnoea. The chapter concludes with a discussion of the importance of machine intelligence and signal processing techniques in developing medical diagnostic device.