Abstract
A lot of feature vectors and sub-band signals are considered for Epileptic seizure classification. Unfortunately, not all the feature vectors and sub-band signals contribute to the final result. In view of this limitation, we propose a modified Differential Evolution Feature Selection algorithm (MDEFS), which searches the best feature vector subset and the sub-band signals to distinguish three groups of subjects (healthy, ictal and interictal). From the experiment results, it is observed that the bagging method based on the optimal feature subset (the standard deviation attribute in the delta sub-band signal, the time-lag attribute in the delta sub-band signal, fractal dimension in the alpha sub-band signal, the correlation dimension attribute in the alpha sub-band signal and the standard deviation attribute in the beta sub-band signal) selected by MDEFS results in highest classification accuracy of 98.67 %.
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Glossary of Terms/Acronyms
Glossary of Terms/Acronyms
- DWT:
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Discrete Wavelet Transform
- Ictal:
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State or event epileptic seizure
- Interictal:
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State or event between epileptic seizures
- SZN:
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The dataset that seizure is denoted S, healthy Z and interictal is N
- LMBPNN:
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Levenberg-Marquadt Back Propagation Neural Network
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Alzami, F., Wang, D., Yu, Z., You, J., Wong, HS., Han, G. (2016). Robust Epileptic Seizure Classification. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_32
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DOI: https://doi.org/10.1007/978-3-319-42294-7_32
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