Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/76334
Title: Machine learning methods for diagnosis of epilepsy from EEG
Authors: Roshini, Koppala
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Abstract: Epilepsy is a neurological disorder presented with unpredicted and repeated seizures due to abnormal electrical activity in the brain. They can be diagnosed by analysing the electroencephalogram (EEG), which shows spikes when there is epileptic activity. The aim of this dissertation; “MACHINE LEARNING METHODS FOR DIAGNOSIS OF EPILEPSY FROM EEG”, is to develop a generic system which will be able to predict if the patient is epileptic. The system is built using Machine Learning algorithms, like k-Nearest Neighbour, Neural Networks and Convolutional Neural Networks. The algorithm is trained on interictal scalp EEG data recorded from epileptic patients. The project is in collaboration with neurologists at Massachusetts General Hospital and Harvard Medical School and applied Mathematicians at MIT. Once the EEG of the patient is fed to the system it should go through stream of independent processes and finally assess if the patient is potentially positive for epilepsy.
URI: http://hdl.handle.net/10356/76334
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
KoppalaRoshini_2018.pdf
  Restricted Access
Main article2.66 MBAdobe PDFView/Open

Page view(s) 5

69
checked on Oct 20, 2020

Download(s) 5

14
checked on Oct 20, 2020

Google ScholarTM

Check

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