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...
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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 |
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DRNTU::Engineering::Electrical and electronic engineering Roshini, Koppala Machine learning methods for diagnosis of epilepsy from EEG |
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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 |