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- ISSN: 1872-2121
- E-ISSN: 2212-4047
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Genetic Algorithm-based Machine Learning Approach for Epileptic Seizure Identification and Classification
- Source: Recent Patents on Engineering, Volume 19, Issue 2, Jan 2025, E250823220377
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- 14 Feb 2023
- 20 Jul 2023
- 01 Feb 2025
Abstract
Background
Epileptic Seizure (ES) is a neural disorder that generates an uncontrolled brain signal impulse. The disorder is seen in young children and adults with a positive medical history.
Methods
In this patent paper, a novel approach to epileptic seizure detection and prediction is proposed and evaluated. The seizure is retrained from Electroencephalography (EEG) high-dimension datasets. The EEG datasets further segment features of interdependent EEG into a matrix. This matrix is linked to providing a validation occurrence of similar feature events with a minimum redundancy maximum relevance (MRMR) approach for ES feature optimization.
Result and Discussion
The uncertainty-based genetic algorithm for parametric evaluation and validation (GAPEr) is used for predictive analysis and decision support via a dedicated neural networking model. The sizer detection and prediction are supported and validated via a series of interactions from trained datasets.
Conclusion
The proposed setup has achieved higher accuracy and dependency in decision support of Epileptic Seizure identification and classification based on predictive evaluation.
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