A new approach for diagnostic estimation of Obstructive Sleep Apnea Syndrome based on One Dimensional Local Binary Pattern

Abstract

In this study, a new approach for estimation of Obstructive Sleep Apnea Syndrome (OSAS) was proposed. OSAS is a sleep disorder that affects the life comfortability in human life. Up to now, the OSAS was diagnosed by Polysomnography (PSG) device by connected to the patients via electrodes. This device is expensive and restricted in the clinics. Since OSAS is serious, it should be diagnosed and treated early. For this purpose, the recorded Electroencephalography (EEG), Electromyography (EMG) and snore data were analyzed and features of them extracted by a proposed method called One Dimensional Local Binary Pattern (1D-LBP). The 1D-LBP extracted features from raw data effectively. The features, then, were fed to classifier's input in order to diagnose OSAS. As a result most of tested classifiers have yielded accuracies over 99%. The best results were obtained by using EEG, EMG and snore signal altogether. It was also shown that while the complexity of signal increase the best accuracy was obtained at the output of the classifier. The results have shown that the 1D-LBP method is an acceptable and has advantageous over conventional methods due to its capable of extract significant features from more complex signal. The results can be used in sleep laboratory for help to experts before put patient to the PSG.