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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

ELM architecture
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Fig3: ELM architecture

Mentions: The architecture of an ELM is shown in Fig. 3. The network consists of three layers, i.e. the input, hidden, and output layers. Between the input and hidden layers are the input weights, and between the hidden and output layers are the output weights. The training process of an ELM proceeds in three stages. In the first stage, the input weights are assigned randomly between −1 and 1, and the biases in the hidden layer are assigned randomly between 0 and 1. Both of these parameters remain fixed during the training process. Afterward, the output matrix of the hidden layer, H, is calculated as follows:Fig. 3


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)

ELM architecture
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: ELM architecture
Mentions: The architecture of an ELM is shown in Fig. 3. The network consists of three layers, i.e. the input, hidden, and output layers. Between the input and hidden layers are the input weights, and between the hidden and output layers are the output weights. The training process of an ELM proceeds in three stages. In the first stage, the input weights are assigned randomly between −1 and 1, and the biases in the hidden layer are assigned randomly between 0 and 1. Both of these parameters remain fixed during the training process. Afterward, the output matrix of the hidden layer, H, is calculated as follows:Fig. 3

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