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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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/76334 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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