Increased electrical activity in the brain causes epilepsy, which causes seizures resulting in various medical complications that can sometimes be fatal. Doctors use electroencephalography (EEG) for profiling and diagnosis of epilepsy. According to the World Health Organization (WHO), approximately 50 million people worldwide have epilepsy, making it one of the most common neurological disorders globally. This number represents about 0.7% of the global population. The conventional method of EEG analysis employed by medical professionals is a visual investigation that is time-consuming and requires expertise because of the variability in EEG signals. This paper describes a method for detecting epileptic seizures in EEG signals by combining signal processing and machine learning techniques. SVM and other machine learning techniques detect anomalies in the input EEG signal. To extract features, DWT is used for decomposition to sub-bands. The proposed method aims to improve the accuracy of the machine learning model while using as few features as possible.

The classification results show an accuracy of 100% with just one feature, Mean Absolute Value, from datasets A&E. With additional features, the overall accuracy remains high at 99%, with specificity and sensitivity values of 97.2% and 99.1%, respectively. These results outperform previous research on the same dataset, demonstrating the effectiveness of our approach. This research contributes to developing more accurate and efficient epilepsy diagnosis systems, potentially improving patient outcomes.