@article{article_1048332, title={EEG Based Automatic Sleep Staging via Simple 2D-Convolutional Neural Network}, journal={Gazi Journal of Engineering Sciences}, volume={8}, pages={491–498}, year={2022}, author={Kaya, İbrahim}, keywords={Automatic Sleep Staging, EEG, Deep Learning, Convolutional Neural Network (CNN).}, abstract={Sleep disorders have high prevalence and cause various health problems. For the diagnostics of these disorders and assessment of the sleep quality, many physiological data are collected using polysomnogram (PSG) method. The most important PSG data is the EEG recorded from the brain during sleep. Analysis of hours of sleep EEG data by experts is an onerous task which requires high attention. Recently, many automatic sleep staging classifiers using EEG are developed in order to prevent human error, and to provide a quick objective analysis. They use machine learning techniques and predict the sleep stage of each EEG epoch. Compared to traditional machine learning, deep learning which requires no hand-crafted feature extraction was able to classify sleep stages better. 1D Convolutional Neural Networks (CNN) are the main methods used in automatic sleep staging recently. In this research a simple 2D-CNN based automatic sleep staging feasibility is investigated. It has been found that a 2D CNN can classify the sleep stages by accuracy of 92.55% and with a Cohen’s kappa of 0.82.}, number={3}, publisher={Parantez Teknoloji}