Classification of different type of seizure using machine learning approach
The following Final Year Project gives an overview of the work done by the student for Classification of different types of seizures using machine learning approach, with different methods avaliable. The report consists of 6 chapters – Chapter 1: Objectives of the project and introduction of EEG bac...
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| Online Access: | http://hdl.handle.net/10356/78141 |
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sg-ntu-dr.10356-781412023-07-07T16:31:02Z Classification of different type of seizure using machine learning approach Low, Li Yian Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The following Final Year Project gives an overview of the work done by the student for Classification of different types of seizures using machine learning approach, with different methods avaliable. The report consists of 6 chapters – Chapter 1: Objectives of the project and introduction of EEG backgrounds, Chapter 2: Pre-processing and seizure segments, Chapter 3: Feature Extraction methods and results, Chapter 4: Feature Selection and results, Chapter 5: Classification and final results, Chapter 6: Conclusion on the results, future work and reflection. Bachelor of Engineering (Information Engineering and Media) 2019-06-12T07:38:00Z 2019-06-12T07:38:00Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78141 en Nanyang Technological University 66 p. application/pdf |
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Nanyang Technological University |
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Asia |
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Singapore Singapore |
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English |
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DRNTU::Engineering::Electrical and electronic engineering |
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DRNTU::Engineering::Electrical and electronic engineering Low, Li Yian Classification of different type of seizure using machine learning approach |
| description |
The following Final Year Project gives an overview of the work done by the student for Classification of different types of seizures using machine learning approach, with different methods avaliable. The report consists of 6 chapters – Chapter 1: Objectives of the project and introduction of EEG backgrounds, Chapter 2: Pre-processing and seizure segments, Chapter 3: Feature Extraction methods and results, Chapter 4: Feature Selection and results, Chapter 5: Classification and final results, Chapter 6: Conclusion on the results, future work and reflection. |
| author2 |
Justin Dauwels |
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Justin Dauwels Low, Li Yian |
| format |
Final Year Project |
| author |
Low, Li Yian |
| author_sort |
Low, Li Yian |
| title |
Classification of different type of seizure using machine learning approach |
| title_short |
Classification of different type of seizure using machine learning approach |
| title_full |
Classification of different type of seizure using machine learning approach |
| title_fullStr |
Classification of different type of seizure using machine learning approach |
| title_full_unstemmed |
Classification of different type of seizure using machine learning approach |
| title_sort |
classification of different type of seizure using machine learning approach |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10356/78141 |
| _version_ |
1772825722591641600 |