Dingle's Model-based EEG Peak Detection using a Rule-based Classifier
The employment of peak detection algorithm is prominent in several clinical applications such as diagnosis and treatment of epilepsy patients, assisting to determine patient syndrome, and guiding paralyzed patients to manage some devices. In this study, the performances of four different peak model...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/8242/ http://umpir.ump.edu.my/id/eprint/8242/1/fkee-2015-Zuwairie-Dingles%20Model-based%20EEG.pdf |
| _version_ | 1848818189723500544 |
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| author | Asrul, Adam Norrima, Mokhtar Marizan, Mubin Zuwairie, Ibrahim Mohd Ibrahim, Shapiai |
| author_facet | Asrul, Adam Norrima, Mokhtar Marizan, Mubin Zuwairie, Ibrahim Mohd Ibrahim, Shapiai |
| author_sort | Asrul, Adam |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The employment of peak detection algorithm is prominent in several clinical applications such as diagnosis and
treatment of epilepsy patients, assisting to determine patient syndrome, and guiding paralyzed patients to manage some devices. In this study, the performances of four different peak models of time domain approach which are Dumpala's, Acir's, Liu's, and Dingle's peak models are evaluated for electroencephalogram (EEG) signal peak detection algorithm. The algorithm is developed into three stages: peak candidate detection, feature extraction, and classification. Rule-based classifier with an estimation technique based on particle swarm optimization (PSO) is employed in the classification stage. The evaluation result shows that the best peak model is Dingle's peak model with the highest test performance is 88.78%.
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| first_indexed | 2025-11-15T01:33:43Z |
| format | Conference or Workshop Item |
| id | ump-8242 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:33:43Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-82422018-02-08T00:47:28Z http://umpir.ump.edu.my/id/eprint/8242/ Dingle's Model-based EEG Peak Detection using a Rule-based Classifier Asrul, Adam Norrima, Mokhtar Marizan, Mubin Zuwairie, Ibrahim Mohd Ibrahim, Shapiai TK Electrical engineering. Electronics Nuclear engineering The employment of peak detection algorithm is prominent in several clinical applications such as diagnosis and treatment of epilepsy patients, assisting to determine patient syndrome, and guiding paralyzed patients to manage some devices. In this study, the performances of four different peak models of time domain approach which are Dumpala's, Acir's, Liu's, and Dingle's peak models are evaluated for electroencephalogram (EEG) signal peak detection algorithm. The algorithm is developed into three stages: peak candidate detection, feature extraction, and classification. Rule-based classifier with an estimation technique based on particle swarm optimization (PSO) is employed in the classification stage. The evaluation result shows that the best peak model is Dingle's peak model with the highest test performance is 88.78%. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8242/1/fkee-2015-Zuwairie-Dingles%20Model-based%20EEG.pdf Asrul, Adam and Norrima, Mokhtar and Marizan, Mubin and Zuwairie, Ibrahim and Mohd Ibrahim, Shapiai (2015) Dingle's Model-based EEG Peak Detection using a Rule-based Classifier. In: Proceedings of the 2015 International Conference on Artificial Life and Robotics (ICAROB 2015) , 10-12 January 2015 , Oita, Japan. pp. 1-4.. (Published) |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Asrul, Adam Norrima, Mokhtar Marizan, Mubin Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Dingle's Model-based EEG Peak Detection using a Rule-based Classifier |
| title | Dingle's Model-based EEG Peak Detection using a Rule-based Classifier |
| title_full | Dingle's Model-based EEG Peak Detection using a Rule-based Classifier |
| title_fullStr | Dingle's Model-based EEG Peak Detection using a Rule-based Classifier |
| title_full_unstemmed | Dingle's Model-based EEG Peak Detection using a Rule-based Classifier |
| title_short | Dingle's Model-based EEG Peak Detection using a Rule-based Classifier |
| title_sort | dingle's model-based eeg peak detection using a rule-based classifier |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/8242/ http://umpir.ump.edu.my/id/eprint/8242/1/fkee-2015-Zuwairie-Dingles%20Model-based%20EEG.pdf |