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

Overview of attention for article published in SpringerPlus, July 2016
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Title
Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal
Published in
SpringerPlus, July 2016
DOI 10.1186/s40064-016-2697-0
Pubmed ID
Authors

Adam, Asrul, Ibrahim, Zuwairie, Mokhtar, Norrima, Shapiai, Mohd Ibrahim, Cumming, Paul, Mubin, Marizan, Asrul Adam, Zuwairie Ibrahim, Norrima Mokhtar, Mohd Ibrahim Shapiai, Paul Cumming, Marizan Mubin

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.

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Mendeley readers

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The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 24%
Student > Bachelor 6 18%
Professor > Associate Professor 3 9%
Lecturer 2 6%
Researcher 2 6%
Other 2 6%
Unknown 11 32%
Readers by discipline Count As %
Engineering 5 15%
Computer Science 5 15%
Neuroscience 4 12%
Psychology 3 9%
Medicine and Dentistry 2 6%
Other 4 12%
Unknown 11 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 July 2016.
All research outputs
#18,466,238
of 22,881,154 outputs
Outputs from SpringerPlus
#1,262
of 1,851 outputs
Outputs of similar age
#270,847
of 354,304 outputs
Outputs of similar age from SpringerPlus
#160
of 227 outputs
Altmetric has tracked 22,881,154 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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