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Volume 17 , Issue 2 , PP: 219-231, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Suresh Nalla 1 * , Seetharam Khetavath 2
Doi: https://doi.org/10.54216/FPA.170217
Epilepsy is a neural condition that is rather prevalent and affects a sizeable portion of the average population all over the world. Throughout its history, the illness has constantly be located of significant status in the pitch of biomedicine due to the dangers it poses to people's health. Electroencephalogram (EEG) recordings are a method that may be utilized to evaluate epilepsy, which is defined by the occurrence of seizures that occur repeatedly and without any apparent cause. Electroencephalography, often known as EEG, is a method that is utilized to assess the electric movement located within the brain. The examination of electroencephalogram data is an essential component in the field of epilepsy research, since it allows for the early detection of epileptic episodes. On the other hand, the generation of models that are independent of individual characteristics is a significant challenge. Extensive efforts have been directed to the creation of classifiers that are tailored to specific patients. In this thesis, the cross-patient viewpoint is the primary focus of investigation; nevertheless, the heterogeneity of EEG patterns among people presents a challenge to this investigation. An examination of the similarities and differences of the pattern recognition algorithms that are applied for the diagnosis of epileptic episodes based on EEG data was taken. SVM (Support Vector Machine) and KNN (K-Nearest Neighbor) were the approaches that were under consideration for evaluation. According to the findings of our analysis, the two approaches exhibit comparable levels of performance; however, KNN attained a slightly greater level of accuracy in some situations on occasion.
Epilepsy, EEG, SVM, KNN, Convolutional neural networks
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