Machine learning methods for diagnosis of epilepsy from EEG

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 LEA...

Full description

Saved in:
Bibliographic Details
Main Author: Roshini, Koppala
Other Authors: Justin Dauwels
Format: Theses and Dissertations
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/76334
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-76334
record_format dspace
spelling sg-ntu-dr.10356-763342023-07-04T15:40:19Z Machine learning methods for diagnosis of epilepsy from EEG Roshini, Koppala Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering 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. Master of Science (Computer Control and Automation) 2018-12-19T14:39:57Z 2018-12-19T14:39:57Z 2018 Thesis http://hdl.handle.net/10356/76334 en 76 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Roshini, Koppala
Machine learning methods for diagnosis of epilepsy from EEG
description 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.
author2 Justin Dauwels
author_facet Justin Dauwels
Roshini, Koppala
format Theses and Dissertations
author Roshini, Koppala
author_sort Roshini, Koppala
title Machine learning methods for diagnosis of epilepsy from EEG
title_short Machine learning methods for diagnosis of epilepsy from EEG
title_full Machine learning methods for diagnosis of epilepsy from EEG
title_fullStr Machine learning methods for diagnosis of epilepsy from EEG
title_full_unstemmed Machine learning methods for diagnosis of epilepsy from EEG
title_sort machine learning methods for diagnosis of epilepsy from eeg
publishDate 2018
url http://hdl.handle.net/10356/76334
_version_ 1772827647903006720