Al-Azzawi, Athar Hussein AliAl-Jumaili, SaifIbrahim, Abdullahi AbduDuru, Adil Deniz2023-01-082023-01-082022Al-azzawi, A. H. A. L., Al-jumaili, S., Ibrahim, A. A., Duru, A. D. (2022). Classification of epileptic seizure features from scalp electrical measurements using KNN and SVM based on Fourier Transform. In AIP Conference Proceedings (Vol. 2499, No. 1, p. 020003). AIP Publishing LLC.9780735442863https://hdl.handle.net/20.500.12939/3175Epilepsy classification techniques are one of the areas that are still under searching till now as long as there is no specific method for detection seizures. The brain consists of more than 100 billion nerves that generate electrical activity. These activities are recorded using an Electroencephalogram (EEG) by electrodes attached to the scalp. EEG is considered a big footstep in the medical and technical field where it allows the detection of brain disorders. However, this paper aims to identify the most efficient classification algorithm for classifying EEG signals of epileptic seizures. Therefore, we applied two classification techniques namely Support Vector Machine (SVM) and k-Nearest Neighbors (KNN), which rely on the features extracted from the data by the Fast Fourier Transform (FFT) method. The results show SVM obtained the highest accuracy value compared to KNN, accurate scores were 99.5% and 99%, respectively.eninfo:eu-repo/semantics/closedAccessElectroencephalogram (EEG)Fast Fourier Transform (FFT)K-Nearest Neighbors (KNN)Support Vector Machine (SVM)Conference Object24992-s2.0-85144015403Q4