Fourier transform based epileptic seizure features classification using scalp electrical measurements using KNN and SVM
| dc.contributor.advisor | Ibrahim, Abdullahi Abdu | |
| dc.contributor.author | Al-Azzawi, Athar | |
| dc.date.accessioned | 2022-04-17T11:29:21Z | |
| dc.date.available | 2022-04-17T11:29:21Z | |
| dc.date.issued | 2021 | en_US |
| dc.date.submitted | 2021 | |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalı | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.citation | 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. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/2355 | |
| dc.identifier.yoktezid | 683846 | |
| dc.institutionauthor | Al-Azzawi, Athar | |
| dc.language.iso | en | |
| dc.publisher | Altınbaş Üniversitesi | en_US |
| dc.relation.publicationcategory | Tez | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Electroencephalogram (EEG) | en_US |
| dc.subject | Fast Fourier Transform (FFT) | en_US |
| dc.subject | K-Nearest Neighbor (KNN) | en_US |
| dc.subject | Support Vector Machine (SVM) | en_US |
| dc.title | Fourier transform based epileptic seizure features classification using scalp electrical measurements using KNN and SVM | |
| dc.type | Master Thesis |
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