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Epileptic Seizure Classification Using Feed Forward Neural Network Based on Parametric Features
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| Author:
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RAJENDRAN T, SRIDHAR K P
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| Abstract:
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Globally, epilepsy is a severe neural disorder occurring among 0.6-0.8% of the population. The formation of patternchange
from normal to disturbed factors that all gets triggered at once is called seizure. Many researchers introduced
different techniques, but the problem of detecting epileptic seizures remains unsolved. This paper presents a new
technique for detection of epileptic seizure-based Electroencephalogram (EEG) signals. The detection scheme adapts
the non-invasive measure of the brain’s electrical activity by placing the electrodes on the scalp. The collection of such
electrical activity and diagnosing is a complex task because the brain is composed of numerous classes with numerous
overlying features. Feature extraction based on parametric and non-parametric method is employed to extract the
features vectors from EEG signals. The extracted features are forwarded to machine learning algorithms. Feed
Forward Neural Network (FFNN) is implemented to detect the epileptic seizure. The performance results are
evaluated by comparison of previous Modified Back Propagation Neural Network, Multilayer perceptron neural
network, combined neural network, Probabilistic Neural Network and FFNN methods with respect to feature
extraction in terms of accuracy, specificity and sensitivity. The FFNN has the higher classification accuracy as 97.23%
demonstrates that it has a great potentiality of the real-time epileptic seizure detection.
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| Keyword:
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Epilepsy Seizure, Electroencephalogram, Feature Extraction, Neural Network and Auto Regressive
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2018.10.04.046
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| Download:
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