Muhammed K. UCAR

Work place: Electrical-Electronics Engineering, Sakarya University, Esentepe, Sakarya 54187, Turkey

E-mail: mucar@sakarya.edu.tr

Website:

Research Interests: Image Processing, Pattern Recognition, Computer systems and computational processes

Biography

Muhammed Kür┼čad Uçar was born in Gumushane, Turkey. He received his Electrical and Electronics Engineering degree from the Mustafa Kemal University, Turkey. He graduated from Sakarya University with a Masters in Electrical and Electronic Engineering. He received his Ph.D. degree from the same university in 2017. Currently, he is a Research Assistant doctor in the Dept. of Electrical and Electronics Engineering at Sakarya University. His research areas include biomedical signal classification, statistical signal processing, digital signal processing, pattern recognition, classification and Prediction Systems

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: https://doi.org/10.5815/ijigsp.2017.07.01, 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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