Detection of sleep apnea through ECG signal features

Abstract

A type of sleep disorder is characterized by the breaks in respiratory breathing process or occurrences of uncommon or superficial breathing during sleep is called sleep apnea disorder. Since for the last few years various signals (such as PolySomnoGraphy (PSG) signals, EEG signals, Respiratory signals, ECG signals etc) have been used for the detection of sleep apnea syndrome or disorder. But ECG signal recordings have been found effective and efficient for the diagnosis process. Various models and algorithms have been developed using ECG signal recordings to detect sleep apnea disorder. Sleep apnea is a sleep related breathing disorder that affects middle aged adults. Most of sleep apnea cases are currently undiagnosed. Due to expenses and limitations of Polysomnography at sleep labs. In new techniques for sleepapnea classification are being developed for most comfortable and timely detection. Which processes short duration epochs of the electrocardiogram data. The automated classification algorithm are based on Support Vector Machines (SVM) and tested for apnea recordings. ECG signal is the most important and powerful tool used contains the diagnosis and treatment of heart diseases. ECG signal represents electrical activity of the heart. The accurate ECG interpretation is required to evaluate the valuable information inside the ECG signal.