Optimizing Seizure Detection: A Comparative Study of SVM, CNN, and RNN-LSTM

Epilepsy seizures are complex neurological phenomena marked by recurrent and unpredictable seizures that can greatly affect an individual’s quality of life. It affects millions of people worldwide. The exact and timely detection of epileptic seizures is crucial in the management and treatment of epilepsy. Many methods have been put forth recently for the diagnosis of epileptic seizures using magnetic resonance imaging (MRI) and electroencephalography (EEG). This work focuses on using deep learning and machine learning techniques, such as Support Vector Machines (SVMs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), to automatically identify epileptic seizures. These techniques have shown promising results in a variety of fields, including time series data processing and medical image analysis. In this work, we present a unique method for detecting epileptic seizures using electroencephalogram (EEG) data by comparing the outcomes of three deep learning architectures: SVM, CNN, and RNN-LSTM (Long-short term memory). The experimental results demonstrate that the SVM, CNN and RNN-LSTM models exhibit promising performance in detecting epileptic seizures from EEG data.