Classification of Epileptic Seizure Features from Scalp Electrical Measurements Using KNN and SVM Based on Fourier Transform

Al-Azzawi A. H. A., Al-Jumaili S., Ibrahim A. A., Duru A. D.

2nd International Conference on Information Technology, Advanced Mechanical and Electrical Engineering, ICITAMEE 2021, Yogyakarta, Endonezya, 25 - 26 Ağustos 2021, cilt.2499 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2499
  • Doi Numarası: 10.1063/5.0105034
  • Basıldığı Şehir: Yogyakarta
  • Basıldığı Ülke: Endonezya
  • Anahtar Kelimeler: Electroencephalogram (EEG), Fast Fourier Transform (FFT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM)
  • Marmara Üniversitesi Adresli: Evet

Özet

© 2022 American Institute of Physics Inc.. All rights reserved.Epilepsy 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.