Please use this identifier to cite or link to this item: 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
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Ye Ruofan_FYP report.pdf
  Restricted Access
1.63 MBAdobe PDFView/Open

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.