Work place: Technical Sciences Vocational School, Amasya University, Amasya, 05189, Turkey



Research Interests: Engineering, Computational Engineering, Computational Science and Engineering


Erhan BERGİL was born in Sivas, Turkey. He received the B.Sc. and M.Sc. degrees in Electrical-Electronics Engineering from Faculty of Engineering, Ondokuz Mayıs University, Turkey in 2001 and 2009 respectively. He continues his Ph.D. degree in Electrical-Electronics Engineering, Sakarya University, Turkey. His research areas include signal processing and electronic systems. He has been working as a instructor in Amasya University, Turkey.

Author Articles
Automated Pre-Seizure Detection for Epileptic Patients Using Machine Learning Methods

By Sevda GUl Muhammed K. UCAR Gokcen Cetinel Erhan BERGIL Mehmet R. BOZKURT

DOI:, Pub. Date: 8 Jul. 2017

Epilepsy is a neurological disorder resulting from unusual electrochemical discharge of nerve cells in the brain, and EEG (Electroencephalography) signals are commonly used today to diagnose the disorder that occurs in these signals. In this study, it was aimed to use EEG signals to automatically detect pre-epileptic seizure with machine learning techniques. EEG data from two epileptic patients were used in the study. EEG data is passed through the preprocessing stage and then subjected to feature extraction in time and frequency domain. In the feature extraction step 26 features are obtain to determine the seizure time. When the feature vector is analyzed, it is observed that the characteristics of the pre-seizure and non-seizure period are unevenly distributed. A systematic sampling method has been applied for this imbalance. For the balanced data, two test sets with and without Eta correlation are established. Finally, the classification process is performed using the k-Nearest Neighbor classification method. The obtained data are evaluated in terms of Eta-correlated and uncorrelated accuracy, error rate, precision, sensitivity and F-criterion for each channel. 

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