Dingle's Model-based EEG Peak Detection using a Rule-based Classifier

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...

Full description

Bibliographic Details
Main Authors: Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Zuwairie, Ibrahim, Mohd Ibrahim, Shapiai
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
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
id ump-8242
recordtype eprints
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..
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TK Electrical engineering. Electronics Nuclear engineering
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
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%.
format Conference or Workshop Item
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
title 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_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_sort dingle's model-based eeg peak detection using a rule-based classifier
publishDate 2015
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
first_indexed 2023-09-18T22:05:36Z
last_indexed 2023-09-18T22:05:36Z
_version_ 1777414686944264192

Similar Items