Abstract
Epileptic seizure (ES) is caused due to the unpredictable and imbalanced discharge of electric signals causing a muscle ruptures. The instance is critical if unattended medically. In the proposed paper, a feature optimization and classification technique is discussed. The technique is based on the dynamic feature set extraction and producing cluster based on categorization labels. The technique is structured on grey-wolf optimization algorithm in identifying the highlighted feature–attribute co-relationship. The technique has processed attribute inter-connectivity coordinates in creating a virtual mapping and labeling of cluster-heads to provide seizure severity. The technique has successfully adopted multi-dimensional datasets for improved performance and calibration under inter-dependent attribute-feature mapping. The technique has achieved 96.76% accuracy on trained datasets with 98.76% sensitivity and 97.86% in precision on epileptic seizure classification for decision-making.
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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.
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Thanuja, K., Shoba, M. & Patil, K. Epileptic Seizure Classification and Feature Optimization Technique Using Grey Wolf Algorithm on Dynamic Datasets. SN COMPUT. SCI. 4, 311 (2023). https://doi.org/10.1007/s42979-023-01741-0
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DOI: https://doi.org/10.1007/s42979-023-01741-0