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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

The eight points of peak model
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Fig2: The eight points of peak model

Mentions: Determining the local maxima (peak points) and minima (valley points), the first process in determining a candidate peak, can be performed using an algorithm developed by Billauer (2012). The subsequent process of detecting a peak candidate is as follows: By considering a discrete-time signal, x(I) of L points, the ith candidate peak point, PPi, is identified using the three-point sliding window method (Dumpala et al. 1982). The three selected points are denoted as x(I−1), x(I) and x(I + 1) for I = 1, 2, 3, …,L. A candidate peak point is identified when x(PPi−1) < x(PPi) > x(PPi + 1) and two associated valley points, VP1i and VP2i lie on either side of the peak, as shown in Fig. 2. The valley points are defined when x(VP1i−1) > x(VP1i) < x(VP1i + 1) and x(VP2i−1) > x(VP2i) < x(VP2i + 1).Fig. 2


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)

The eight points of peak model
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: The eight points of peak model
Mentions: Determining the local maxima (peak points) and minima (valley points), the first process in determining a candidate peak, can be performed using an algorithm developed by Billauer (2012). The subsequent process of detecting a peak candidate is as follows: By considering a discrete-time signal, x(I) of L points, the ith candidate peak point, PPi, is identified using the three-point sliding window method (Dumpala et al. 1982). The three selected points are denoted as x(I−1), x(I) and x(I + 1) for I = 1, 2, 3, …,L. A candidate peak point is identified when x(PPi−1) < x(PPi) > x(PPi + 1) and two associated valley points, VP1i and VP2i lie on either side of the peak, as shown in Fig. 2. The valley points are defined when x(VP1i−1) > x(VP1i) < x(VP1i + 1) and x(VP2i−1) > x(VP2i) < x(VP2i + 1).Fig. 2

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