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Integrated Computer-Aided Engineering
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Integrated Computer-Aided Engineering
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Integrated Computer-Aided Engineering
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Integrated Computer-Aided Engineering
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Engineering Applications of Artificial Intelligence
A spike detection method in EEG based on improved morphological filter
Computers in Biology and Medicine
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
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Integrated Computer-Aided Engineering
Computers in Biology and Medicine
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Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
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ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Neural Processing Letters
NeuCube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
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Neural Computation
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Integrated Computer-Aided Engineering
A supervised method for microcalcification cluster diagnosis
Integrated Computer-Aided Engineering
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The goal of this research is to develop an efficient SNN model for epilepsy and epileptic seizure detection using electroencephalograms (EEGs), a complicated pattern recognition problem. Three training algorithms are investigated: SpikeProp (using both incremental and batch processing), QuickProp, and RProp. Since the epilepsy and epileptic seizure detection problem requires a large training dataset the efficacy of these algorithms is investigated by first applying them to the XOR and Fisher iris benchmark problems. Three measures of performance are investigated: number of convergence epochs, computational efficiency, and classification accuracy. Extensive parametric analysis is performed to identify heuristic rules and optimum parameter values that increase the computational efficiency and classification accuracy. The result is a remarkable increase in computational efficiency. For the XOR problem, the computational efficiency of SpikeProp, QuickProp, and RProp is increased by a factor of 588, 82, and 75, respectively, compared with the results reported in the literature. EEGs from three different subject groups are analyzed: (a) healthy subjects, (b) epileptic subjects during a seizure-free interval, and (c) epileptic subjects during a seizure. It is concluded that RProp is the best training algorithm because it has the highest classification accuracy among all training algorithms specially for large size training datasets with about the same computational efficiency provided by SpikeProp. The SNN model for EEG classification and epilepsy and seizure detection uses RProp as training algorithm. This model yields a high classification accuracy of 92.5%.