Genetic Algorithm-based Machine Learning Approach for Epileptic Seizure Identification and Classification

Title:Genetic Algorithm-based Machine Learning Approach for Epileptic Seizure Identification and Classification

Volume: 19 Issue: 2

Author(s): K. Thanuja*, Shoba M. and Kirankumari Patil

Affiliation:

• Department of ISE, BMS Institute of Technology and Management, Yelahanka, Bangalore, Karnataka, India

Keywords: Epileptic seizure, genetic algorithms, feature extraction, decision support, projective analysis, electric flux generation.

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.

Method: 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.

Cite this article as:

Thanuja K.*, M. Shoba and Patil Kirankumari, Genetic Algorithm-based Machine Learning Approach for Epileptic Seizure Identification and Classification, Recent Patents on Engineering 2025; 19 (2) : e250823220377 . https://dx.doi.org/10.2174/1872212118666230825124237