Optimized Feature Selection for Enhanced Epileptic Seizure Detection
Krishnakumar Sivasankari, Keppana Gowder Thanushkodi and Nambiraj Suguna
Affiliation: Anna University of Technology, Coimbatore, India.
Keywords: EEG (Electroencephalogram), Electrocardiogram (EKG), Electromyogram (EMG), EOG (Electro-oculogram),
Genetic Algorithm (GA), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM).
Genetic algorithm (GA) based feature selection method is an evolving search heuristic, used to provide
solutions to optimization problems. Feature selection is an important aspect that improves classification accuracy. The
main objective of this work is to utilize GA for feature selection by integrating it with a bank of multi-class Support
Vector Machine (SVM) for identification of the effective feature set. The proposed GA based approach finds its
application in epileptic seizure detection. EEG dataset containing artefacts and noise were removed by employing
constrained Independent Component Analysis (cICA) and Stationary Wavelet Transform (SWT). The features of the input
data are constructed in the form of feature vector by FastICA technique. The fitness calculation for the selection of
individuals in the GA is calculated by a Linear Discriminant Analysis (LDA) classifier. The multi-class Support Vector
Machine (SVM) (one-against-all) classifier is used for the validation of the selected features. The samples are taken from
948 patients and the classes are divided as normal, seizure, and seizure-free using artificial neural networks. Experimental
results shows that the GA - multi-SVM feature selection technique can achieve higher accuracies as compared to the case
without feature selection.
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