Limits...
Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

Adam A, Ibrahim Z, Mokhtar N, Shapiai MI, Cumming P, Mubin M - Springerplus (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Training and testing phases of EEG signal peak detection
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4940316&req=5

Fig1: Training and testing phases of EEG signal peak detection

Mentions: The training and testing phases of the EEG signal peak detection algorithm are shown in Fig. 1. The training and testing data that used in this study were collected using two channel EEG recordings from 20 voluntary subjects. In the first stage of peak detection, the training and testing EEG signals must be filtered as input to the algorithm, upon selection of the desired peak model. The training phase of the algorithm involves several processes, namely including peak candidate detection, feature extraction, with definition of model-specific features, and then classification process. The estimation process is performed during this phase to train the network for adjusting the ELM parameters using the learning algorithm of the ELM classifier. In the testing phase, the algorithm follows the same series of processes, and the ELM parameters first determined in the training phase are used in the classification process of the testing phase. The final output of the training and testing phase are the predicted peak points and non-peak points from the identified peak candidates.Fig. 1


Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

Adam A, Ibrahim Z, Mokhtar N, Shapiai MI, Cumming P, Mubin M - Springerplus (2016)

Training and testing phases of EEG signal peak detection
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940316&req=5

Fig1: Training and testing phases of EEG signal peak detection
Mentions: The training and testing phases of the EEG signal peak detection algorithm are shown in Fig. 1. The training and testing data that used in this study were collected using two channel EEG recordings from 20 voluntary subjects. In the first stage of peak detection, the training and testing EEG signals must be filtered as input to the algorithm, upon selection of the desired peak model. The training phase of the algorithm involves several processes, namely including peak candidate detection, feature extraction, with definition of model-specific features, and then classification process. The estimation process is performed during this phase to train the network for adjusting the ELM parameters using the learning algorithm of the ELM classifier. In the testing phase, the algorithm follows the same series of processes, and the ELM parameters first determined in the training phase are used in the classification process of the testing phase. The final output of the training and testing phase are the predicted peak points and non-peak points from the identified peak candidates.Fig. 1

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus