TY - JOUR T1 - Detection of Epileptic Seizures with Different Machine Learning Algorithms Using EEG Signals in Daily Life AU - Sönmezocak, Temel AU - Tunçalp, B. Koray PY - 2025 DA - October Y2 - 2025 DO - 10.17694/bajece.1577914 JF - Balkan Journal of Electrical and Computer Engineering PB - MUSA YILMAZ WT - DergiPark SN - 2147-284X SP - 263 EP - 271 VL - 13 IS - 3 LA - en AB - Today, Electroencephalography (EEG) is commonly used as a diagnostic tool for epilepsy. In this study, an effective method for diagnosing epileptic seizures in non-clinical settings is proposed. To evaluate the performance of this method, EEG data from 7 pediatric patients at Boston Children's Hospital were analyzed using Decision Tree (DT), Linear Discriminant (LD), Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The time and frequency characteristics of the EEG signals were compared. 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