Background rejection techniques for automated spike detection in diagnosis of epilepsy
Epilepsy is regarded as one of the most common neuro-physiological disorders characterised by recurrent, involuntary, paroxysmal seizure activity in the brain. Although some causes of epilepsy are known, the majority of the causes are still unknown and under research. So far epilepsy is diagnose...
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| Format: | Thesis |
| Language: | English |
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2017
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| Online Access: | http://hdl.handle.net/10356/69506 |
| _version_ | 1826118001326292992 |
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| author | Salem Chandrasekaran Harihara Subramaniam |
| author2 | Justin Dauwels |
| author_facet | Justin Dauwels Salem Chandrasekaran Harihara Subramaniam |
| author_sort | Salem Chandrasekaran Harihara Subramaniam |
| collection | NTU |
| description | Epilepsy is regarded as one of the most common neuro-physiological disorders characterised
by recurrent, involuntary, paroxysmal seizure activity in the brain. Although
some causes of epilepsy are known, the majority of the causes are still unknown
and under research.
So far epilepsy is diagnosed manually based on the detection of unusual spikes
present in the spatial-temporal characteristics of the brain signals measured using
Electroencephalogram (EEG). However, this becomes erroneous due to presence of
artefacts and also the random nature of the spike size and shape. In order to overcome
these issues, we need a reliable system that can automatically detect spikes
and thus diagnose epilepsy.
This dissertation work involves the classification of Spike and non-spikes from the
EEG data of patients and rejecting the background. Two major areas of Background
Rejection focussed in this project are feature based rejection using feature pool and
Classifier based rejection using Machine Learning techniques.
Using Background Rejection, at each stage, best features are identified and a feature
ranking table is formulated after a cascade of rejection. The feature selection
method is validated by building a single classifier using these features and the results
are compared. |
| first_indexed | 2024-10-01T04:36:36Z |
| format | Thesis |
| id | ntu-10356/69506 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:36:36Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/695062023-07-04T15:48:16Z Background rejection techniques for automated spike detection in diagnosis of epilepsy Salem Chandrasekaran Harihara Subramaniam Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Epilepsy is regarded as one of the most common neuro-physiological disorders characterised by recurrent, involuntary, paroxysmal seizure activity in the brain. Although some causes of epilepsy are known, the majority of the causes are still unknown and under research. So far epilepsy is diagnosed manually based on the detection of unusual spikes present in the spatial-temporal characteristics of the brain signals measured using Electroencephalogram (EEG). However, this becomes erroneous due to presence of artefacts and also the random nature of the spike size and shape. In order to overcome these issues, we need a reliable system that can automatically detect spikes and thus diagnose epilepsy. This dissertation work involves the classification of Spike and non-spikes from the EEG data of patients and rejecting the background. Two major areas of Background Rejection focussed in this project are feature based rejection using feature pool and Classifier based rejection using Machine Learning techniques. Using Background Rejection, at each stage, best features are identified and a feature ranking table is formulated after a cascade of rejection. The feature selection method is validated by building a single classifier using these features and the results are compared. Master of Science (Computer Control and Automation) 2017-02-01T01:29:31Z 2017-02-01T01:29:31Z 2017 Thesis http://hdl.handle.net/10356/69506 en 61 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Salem Chandrasekaran Harihara Subramaniam Background rejection techniques for automated spike detection in diagnosis of epilepsy |
| title | Background rejection techniques for automated spike detection in diagnosis of epilepsy |
| title_full | Background rejection techniques for automated spike detection in diagnosis of epilepsy |
| title_fullStr | Background rejection techniques for automated spike detection in diagnosis of epilepsy |
| title_full_unstemmed | Background rejection techniques for automated spike detection in diagnosis of epilepsy |
| title_short | Background rejection techniques for automated spike detection in diagnosis of epilepsy |
| title_sort | background rejection techniques for automated spike detection in diagnosis of epilepsy |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/69506 |
| work_keys_str_mv | AT salemchandrasekaranhariharasubramaniam backgroundrejectiontechniquesforautomatedspikedetectionindiagnosisofepilepsy |