Browsing by Author "Onargan, Aysu"

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    Prediction of Sleep Apnea Using Eeg Signals and Machine Learning Algorithms
    (IEEE, 2021) Onargan, Aysu; Gavcar, Busra; Çalışkan, Gülizar; Akan, Aydin
    A lot of research has been done on sleep disorders from past to present. Sleep apnea, which we frequently encounter today, is one of the important sleep disorders that threaten human life. This situation that occurs during sleep also affects the daily life of the individual. Obstructive sleep apnea syndrome (OSAS) is a respiratory tract disorder with a prevalence of almost 4% in men and approximately 2% in women [1]. Snoring and OSA, which are among the breathing problems during sleep, are among the conditions caused by the insufficiency of breathing [2]. The aim of our study is to determine whether the person has OSA by analyzing electroencephalogram (EEG) signals. As we know, many physiological and biological activities occur during sleep. In order to observe these activities, we record the electrical activity that occur in our brain. Thanks to the EEG, we transform these activities into digital data. In this project, EEG signals recorded from 4 patients during sleep were processed on MATLAB. Sleep recordings of different sleep zones marked by the doctor are segmented. The data in the segments are divided into 3 headings as pre-apnea, moment of apnea and post- apnea. The data were processed with signal analysis methods such as empirical mode decomposition (EMD) and intrinsic mode functions (IMFs) were extracted. Attributes were obtained from IMFs again on MATLAB. These features are used for classification in advanced machine learning algorithms as pre-apnea and apnea moment as a set of 2 and as a set of 3 as pre-apnea, apnea moment and postapnea. Using the method, we mentioned provides a practical and fast diagnostic process for patients and doctors in our project. In this project, which aims to accelerate the treatment and diagnosis process in order to support the health of patients, it is aimed to classify OSA by analyzing EEG signals. As a result of our project, the accuracy values of the 2-set are between 47.5% and 71.9%, and the accuracy values of the 3-set are between 33.8% - 63.1%.