It is observed that, ANN classification of EEG signals with AR gives better results and these results can also be used for detecting epileptic seizure.
📌 3 Key Takeaways
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Objective & Hypothesis:The purpose of the work described in this paper is to investigate the use of autoregressive (AR) model by using maximum likelihood estimation (MLE) also interpretation and performance of this method to extract classifiable features from human electroencephalogram (EEG) by using Artificial Neural Networks .(ANNs).
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Key Findings & Data:ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic seizure or non-epileptic seizure.
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Conclusion & Impact:It is observed that, ANN classification of EEG signals with AR gives better results and these results can also be used for detecting epileptic seizure.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
The purpose of the work described in this paper is to investigate the use of autoregressive (AR) model by using maximum likelihood estimation (MLE) also interpretation and performance of this method to extract classifiable features from human electroencephalogram (EEG) by using Artificial Neural Networks .(ANNs). ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic seizure or non-epileptic seizure. It is observed that, ANN classification of EEG signals with AR gives better results and these results can also be used for detecting epileptic seizure.
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Cite This Article
SUBAŞI, A., KIYMIK, M. K., ALKAN, A., KÖKLÜKAYA, E. (2005). Neural network classification of EEG signals by using AR with MLE preprocessing for epileptic seizure detection. Mathematical and Computational Applications, Cilt: 10 - Sayı: 1, 57–70. https://ajindex.com/articles/280128
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