Approximately 50 million people have epilepsyworldwide. Prognosis may vary among patients
depending on their seizure semiology, age of onset, seizure onset location, and features of electroencephalogram
(EEG). Several researchers have focused on EEG patterns and demonstrated that EEG patterns of
individuals with epilepsy can be used to predict prognosis and treatment responses. However, accurate EEG
analysis requires an experienced epileptologist with several years of training, who are often unavailable
in small or medium sized hospitals. In this paper, a novel machine learning (ML) model that accurately
distinguishes Benign Epilepsy with Centrotemporal Spikes (BECTS) from Temporal Lobe Epilepsy (TLE)
is proposed. BECTS and TLE show different seizure types and age of onset, but differential diagnosis can
be challenging due to the similar location and patterns of the EEG spikes. The proposed hybrid machine
learning (HML) model processes the diagnosis in the order of (1) creating feature matrices using statistical
indexes after signal decomposition, (2) processing feature selection using Support Vector Machine (SVM)
technology, and (3) classifying the results through ensemble learning based on decision trees. Simulation was
performed using real patient data of 112 BECTS and 112 TLE EEG signals, where training was performed
using 80% of the data and 20% of the data was used in the performance analysis comparison with the actual
labeled data based on the diagnosis of medical doctors. The performance of the hybrid classi cation model
is compared with other representative ML algorithms, which include logistic regression, KNN, SVM, and
ensemble learning based decision tree. The model proposed in this paper shows an accuracy performance
exceeding 99%, which is higher than the performance obtainable from the other ML classi cation models.
The purpose of this study is to introduce a novel EEG diagnostic system that shows maximum ef ciency to
support clinical real-time diagnosis that can accurately distinguish epilepsy types. Future research will focus
on expanding this ML model to categorize other types of epilepsies beyond BECTS and TLE and implement
the HML diagnostic blockchain database into the hospital system.