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https://hdl.handle.net/10356/70408
Title: | Epileptic seizure detection using EEG | Authors: | Ye, Ruofan | Keywords: | DRNTU::Engineering::Computer science and engineering | Issue Date: | 2017 | Abstract: | Seizures 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. | URI: | http://hdl.handle.net/10356/70408 | Schools: | School of Computer Science and Engineering | Rights: | Nanyang Technological University | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Student Reports (FYP/IA/PA/PI) |
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File | Description | Size | Format | |
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Ye Ruofan_FYP report.pdf Restricted Access | 1.63 MB | Adobe PDF | View/Open |
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