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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

Filtered EEG-based eye movement signal (one peak point per signal)
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Fig4: Filtered EEG-based eye movement signal (one peak point per signal)

Mentions: Figure 4 shows a representative case of filtered EEG signals that are labeled as eye movement signals. The dotted red vertical lines show the actual peak point locations, as assigned by a researcher. The eye movement signal consists of 20 signals for channel C3, 20 signals for channel C4, for duration of 10-s per signal, recorded at 256 Hz for a total of 2560 sampling points per signal. Furthermore, each signal contains one known peak point location, where the known peak pattern represents the eye gaze direction, either to the left or to the right.Fig. 4


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)

Filtered EEG-based eye movement signal (one peak point per signal)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Filtered EEG-based eye movement signal (one peak point per signal)
Mentions: Figure 4 shows a representative case of filtered EEG signals that are labeled as eye movement signals. The dotted red vertical lines show the actual peak point locations, as assigned by a researcher. The eye movement signal consists of 20 signals for channel C3, 20 signals for channel C4, for duration of 10-s per signal, recorded at 256 Hz for a total of 2560 sampling points per signal. Furthermore, each signal contains one known peak point location, where the known peak pattern represents the eye gaze direction, either to the left or to the right.Fig. 4

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