ARTIFICIAL ORIENTED SYSTEM TO IDENTIFY TYPE OF EPILEPSY SEIZURE USING EEG WAVE

Abstract

Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal electrical activity in the brain. Accurate identification of seizure types using Electroencephalogram (EEG) signals plays a crucial role in diagnosis, treatment planning, and monitoring of epilepsy patients. Traditional manual analysis of EEG recordings by neurologists is time-consuming and may lead to variability in interpretation. To overcome these limitations, this project proposes an artificial intelligence-oriented system to identify different types of epilepsy seizures using EEG wave signals through machine learning and signal processing techniques.The proposed system acquires EEG signals from standard datasets or real-time monitoring devices and applies preprocessing techniques such as noise removal, filtering, and normalization to improve signal quality. Feature extraction methods such as wavelet transform, statistical analysis, and frequency domain techniques are used to identify relevant EEG characteristics associated with seizure activity. Machine learning algorithms such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) are employed to classify different types of epileptic seizures accurately. The automated classification system helps neurologists in faster diagnosis and improves decision-making efficiency.The implementation of this intelligent system enables early detection and classification of seizure types with improved accuracy compared to conventional manual analysis methods. It also supports continuous monitoring of patients and reduces diagnostic workload. Thus, the proposed system provides a reliable and efficient solution for automated epilepsy seizure identification using EEG wave analysis in modern healthcare applications