Parvez, Mohammad ZavidMostakim, MoinAhmed, TanvirRahman, TasniaMustaqeem, NabihaPriyo, Jannatul Ferdous Binta KalamShahariar, AhnafSharmin, Shaila2022-07-312022-07-3120222022-01ID 18101460ID 18101435ID 18101329ID 17101315ID 17301229http://hdl.handle.net/10361/17046Cataloged from PDF version of thesis.Includes bibliographical references (pages 47-50).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.Epilepsy, a chronic neurological disorder, causes seizure- a fast, uncontrollable electrical disturbance in the brain. Seizures that last for a long time might result in memory loss, weariness, photo sensitivity, paralysis, or death. The early diagnosis of seizures may assist reducing the severity of damage and can be utilized to aid in the treatment of epilepsy patients. Predicting seizures before they occur is a challenge that many researchers are working to overcome by monitoring the brain’s activity; but achieving high sensitivity and precise prediction remains a barrier. Our objective is to predict seizure accurately by detecting the pre-ictal state that occurs prior to a seizure. We have used the CHB-MIT Scalp EEG Dataset for our research and implemented the research work using Butterworth Bandpass Filter and simple 2D Convolutional Neural Network to differentiate the pre-ictal and inter-ictal signals. We aim to propose a generalized approach for epileptic seizure prediction rather than patient-specific approach. We have achieved accuracy of 89.5%, sensitivity 89.7%, precision 89.0% and area under the curve (AUC) is 89.5% with our proposed model. In addition, we have addressed several researchers’ seizure prediction models, sketched their core mechanism, predictive effectiveness, and compared them with our work. Our long-term goal is to develop an implantable device to with high accuracy and low errors that may effectively warn patients of oncoming seizures to initiate antiepileptic therapy so that those who are afflicted with the epilepsy can enjoy a healthy and risk-free life.50 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Bandpass filterChronic neurological disorderConvolutional neural networkCHB-MIT Scalp EEG DatasetDeep learningEpilepsyGeneralized modelPredictionSeizureNeural networks (Computer science)Cognitive learning theory (Deep learning)Thesis