Determining the Most Powerful Features in the Design of an Automatic Sleep Staging System

Abstract

Spending too much time on manual sleep staging is tiring and challenging for sleep specialists. In addition, experience in sleep staging also creates different decisions for sleep experts. The search for finding an effective automatic sleep staging system has been accelerated in the last few years. There are many studies dealing with this problem but very few of them were conducted with real sleep data. Studies have been carried out on mostly processed and cleaned-ready data sets. In addition, there are few studies in which the data distribution in sleep stages is balanced (equal numbers of epochs from each stage are used), and it is seen that the performance of these studies is quite low compared to other studies. When the literature studies are examined, there is a wide range of studies in which many features are extracted, many feature selection methods are used, many classifiers are applied and various combinations of these are available. For this reason, to determine the best-performing features and the most powerful features, 168 features were extracted from the real EEG, EOG, and EMG signals of 124 patients. These features were selected with 7 different feature selection methods, and classification was carried out with 4 classifiers. In general, the ReliefF feature selection method has performed best, and the Bagged Tree classifier has reached the highest classification accuracy of 67.92% with the use of nonlinear features.