dc.contributor.authorYe, Ruofan
dc.date.accessioned2017-04-24T02:28:24Z
dc.date.available2017-04-24T02:28:24Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/70408
dc.description.abstractSeizures occur at unpredictable times and is usually without warnings. Seizures can be dangerous and potentially life-threatening if left without assistance and treatments. This poses a challenge for medical personnel as immediate assistance is required should a patient suffers from a seizure. This project aims to develop an algorithm to allow detection of epileptic seizures of a patient through the use of electroencephalogram (EEG) signal. This algorithm will determine if the input EEG data is epileptic or not. This algorithm consists of two processes: feature extraction and classification. For this purpose, power spectral density is used to extract features of the EEG signals. Classification is done by using a Support Vector Machine (SVM). With a working algorithm in detection of seizure, future implementation of such detection methods could be used in real-life situations where an alarm could be triggered to notify the medical personnel of a seizure of patient so that immediate response could be activated.en_US
dc.format.extent40 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleEpileptic seizure detection using EEGen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorRajapakse Jagath Chandanaen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US


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