Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
Peak detection is a significant step in analyzing the electroencephalography (EEG) signal because peaks may represent meaningful brain activities. Several approaches can be used for peak point detection such as time domain, frequency domain, time-frequency domain, and nonlinear approaches. The...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/9084/ http://umpir.ump.edu.my/id/eprint/9084/ http://umpir.ump.edu.my/id/eprint/9084/1/fkee-2014-zuwairie-feature%20selection%20and%20classifier.pdf |
| Summary: | Peak detection is a significant step in analyzing
the electroencephalography (EEG) signal because peaks may
represent meaningful brain activities. Several approaches
can be used for peak point detection such as time domain,
frequency domain, time-frequency domain, and nonlinear
approaches. The main intention of this study is to find the significant peak features in time domain approach and this can be done using feature selection methods such as
gravitational search algorithm (GSA) and particle swarm
optimization (PSO). This study focuses on using GSA
method, a new computational intelligence algorithm.
Moreover, a rule-based classifier is employed to distinguish a peak point based on the selected features. Using GSA, the parameter estimation of the classifier and the peak feature selection can be done simultaneously. Based on the experimental results, the significant peak features of the peak detection algorithm were obtained where the average test accuracy is 77.74%. |
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