Description

An encephalogram (EEG) is generally used ancillary test for the diagnosis of epilepsy. The EEG signal contains information about brain electrical activity. Neurologists employ direct visual inspection to identify epileptic form abnormalities and this technique can be time consuming which provides variable results secondary to reader expertise level and is limited to identify the abnormalities. Prediction of the disease can help the patient prevent the onset of seizures with the help of appropriate medication. Since the traditional methods of studying EEG are prone to misdiagnosis, Deep Learning like, Long Short Term Memory Neural Network and Artificial Neural Network algorithms are applied and provide a more accurate diagnosis. In this process, we aim to survey models to better describe methodologies for a high-precision model to predict epilepsy in patients. So the deep learning algorithms are used to predict the normal and seizure classes and generate the result in the metrics like, accuracy, precision, recall and f1-score.