Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal
Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by D...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Springer
2016
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/13660/1/SpringerPlusZuwairie.pdf http://umpir.ump.edu.my/id/eprint/13660/7/fkee-2016-zuwairie-Evaluation%20of%20different%20time%20domain%20peak1.pdf |
| _version_ | 1825823023588966400 |
|---|---|
| author | Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Cumming, Paul Marizan, Mubin |
| author_facet | Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Cumming, Paul Marizan, Mubin |
| author_sort | Asrul, Adam |
| collection | UMP |
| description | Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37–52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model. |
| first_indexed | 2024-03-06T12:05:13Z |
| format | Article |
| id | UMPir13660 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T12:05:13Z |
| publishDate | 2016 |
| publisher | Springer |
| record_format | dspace |
| spelling | UMPir136602018-04-27T07:56:03Z http://umpir.ump.edu.my/id/eprint/13660/ Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Cumming, Paul Marizan, Mubin TK Electrical engineering. Electronics Nuclear engineering Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37–52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model. Springer 2016-07-11 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/13660/1/SpringerPlusZuwairie.pdf application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/13660/7/fkee-2016-zuwairie-Evaluation%20of%20different%20time%20domain%20peak1.pdf Asrul, Adam and Zuwairie, Ibrahim and Norrima, Mokhtar and Mohd Ibrahim, Shapiai and Cumming, Paul and Marizan, Mubin (2016) Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal. SpringerPlus, 5 (1036). pp. 1-14. ISSN 2193-1801. (Published) http://dx.doi.org/10.1186/s40064-016-2697-0 DOI: 10.1186/s40064‐016‐2697‐0 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Cumming, Paul Marizan, Mubin Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal |
| title | Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal |
| title_full | Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal |
| title_fullStr | Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal |
| title_full_unstemmed | Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal |
| title_short | Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal |
| title_sort | evaluation of different time domain peak models using extreme learning machine based peak detection for eeg signal |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/13660/1/SpringerPlusZuwairie.pdf http://umpir.ump.edu.my/id/eprint/13660/7/fkee-2016-zuwairie-Evaluation%20of%20different%20time%20domain%20peak1.pdf |
| work_keys_str_mv | AT asruladam evaluationofdifferenttimedomainpeakmodelsusingextremelearningmachinebasedpeakdetectionforeegsignal AT zuwairieibrahim evaluationofdifferenttimedomainpeakmodelsusingextremelearningmachinebasedpeakdetectionforeegsignal AT norrimamokhtar evaluationofdifferenttimedomainpeakmodelsusingextremelearningmachinebasedpeakdetectionforeegsignal AT mohdibrahimshapiai evaluationofdifferenttimedomainpeakmodelsusingextremelearningmachinebasedpeakdetectionforeegsignal AT cummingpaul evaluationofdifferenttimedomainpeakmodelsusingextremelearningmachinebasedpeakdetectionforeegsignal AT marizanmubin evaluationofdifferenttimedomainpeakmodelsusingextremelearningmachinebasedpeakdetectionforeegsignal |