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    Assessment of Epileptic Seizures and Non- Epileptic Seizures via Wearable Sensors and Priori Detection of Epileptic Seizures
    (2022) Ertuğrul, Ömer Faruk; Sönmez, Yasin; Yılmaz, Musa; Akıl, Esref
    Epilepsy is one the most prevalent neurological disorders whose causes are not exactly known. Diagnosis and treatment of epilepsy are closely related to the patient's story, and the most important indicator is the frequency and severity of seizures. Since the disease does not only affect the patients but also the lives of their environment seriously, it is very important to make the diagnosis and treatment correctly. However, sometimes misrecognition from patients and their relatives, unnecessary epilepsy treatment to the patient in non-epileptic seizures mixed with epileptic seizures, or increasing the dose of the drugs used for the patient are the situations frequently encountered. The so-called video-EEG method is used in the detection and segregation of epileptic / non-epileptic seizures. In this method, the patient is kept in an environment where video recording is continuously taken until the seizure occurs, and EEG, EMG, and ECG records of the patient are taken. When the patient has a seizure, the seizure type is separated by examining these records. In this project, seizure detection and seizure type (epileptic / non- epileptic) detection is aimed to be done by using wearable sensors increasingly applied in the field of health. The achievable benefits from the project and data set will provide a different perspective on the epilepsy illness, as well as reduce the number of epilepsy patients who are not in fact epilepsy patients needing treatment, and keep epileptic seizure recordings constantly in the electronic environment so that the treatment processes are monitored more closely.
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    Öğe
    A novel approach in analyzing traffic flow by extreme learning machine method
    (Strojarski Facultet, 2019) Sönmez, Yasin; Kutlu, Hüseyin; Avcı, Engin
    The objective of this study is to detect abnormal behaviours of moving objects captured in highway traffic flow footages, classify them by using artificial learning methods, and lastly to predict the future thereof (regression). To this end, the system being the object of the design and application consists of three stages. In the first stage, to detect the moving object in the video, background/foreground segmentation method of Mixture of Gaussian (MOG), and to track the moving object, Kalman Filter-Hungarian algorithm method have been used. In the second stage, by using the coordinates of the object, such details as location, distance in terms of time, and speed of the object are obtained, and by using total pixel count data relating to the shape of the object are obtained. The software based on the specifically elaborated algorithm compares these data with the data in the table of rules set down for the road under surveillance, and generates an attribute table comprising anomalies of the objects in the video. In the last stage, however, the data included in the attribute table have been classified and predictions by the artificial learning method, Extreme Learning Machine (ELM) made.