Fourier transform based epileptic seizure features classification using scalp electrical measurements using KNN and SVM
[ X ]
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The great development that took place in the technology of the interaction between humans and
computers led to remarkable and incredible success in many scientific fields. Until this day,
researchers' studies are continuing in this field to reach the highest possible accuracy to obtain
the results that approximate the accuracy of human work. Electroencephalogram (EEG) is one
of the devices that took a wide space and led many studies to amazing results in the field of
recording, analyzing, detecting, and classifying brain signals. Where this technology was able to
monitor the disorders, which happened in the brain and provide the ability to study the state of
health of the brain. In addition, machine learning had Various techniques that were successfully
involved in the classification of EEG signals. Support Vector Machine (SVM) and K-Nearest
Neighbor (KNN) were our specialties here among the most suitable techniques for classifying
EEG data. This thesis aims to build a system to assist clinicians on three levels, first assisting in
patients' automatic monitoring which leads to a digital memory easy to memorize. Secondly,
reducing the work time by Supporting the clinicians' decision which assists acceleration in
getting the goals with the least number of errors. Finally, assisting in identifying the appropriate
medication and medical care that patients may need. Thus, for achieving this purpose, the
dataset used here was from Temple University Hospital Seizure Corpus (TUH) [1]. TUH is
considered one of the largest open-source databases and is the most extensive [2-4]. Moreover,
to ensure the best results preprocessing techniques were implemented to Dataset. There are
many techniques for EEG signal processing that feed the classification models with the best
data. Fast Fourier Transform (FFT) was studied as one of the types of feature extraction
methods for processing EEG signals. Eventually, the results associated with classifying seizure
types showed SVM got the best classification accuracy compared with KNN where the
accuracy was 99.5 % and 99 %, respectively.
Açıklama
Anahtar Kelimeler
Electroencephalogram (EEG), Fast Fourier Transform (FFT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM)
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Al-Azzawi, A. (2021). Fourier transform based epileptic seizure features classification using scalp electrical measurements using KNN and SVM (Yayınlanmış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim İstanbul.
