Intelligent Artificial Ants based feature extraction from wavelet packet coefficients for biomedical signal classification

Abstract

In this paper, a new feature extraction method utilizing ant colony optimization in the selection of wavelet packet transform (WPT) best basis is presented and adopted in classifying biomedical signals. The new algorithm, termed Intelligent Artificial Ants (IAA) searches the wavelet packet tree for subsets of features that best interact together thus producing high classification accuracies. While traversing the WPT tree, care is taken so that no redundancy in the information is selected by the Ants. The IAA method is a mixture of filter and wrapper approaches in feature subset selection. The pheromone that the ants lay down is updated by means of an estimation of the information contents of a single feature or feature subset. The significance of the subsets selected by the ants is measured using linear discriminant analysis (LDA) classifier. The IAA method is tested on one of the most important biosignal driven applications, which is the Brain Computer Interface (BCI) problem with 56 EEG channels. Practical results indicate the significance of the proposed method achieving a maximum accuracy of 83%.

Feature Extraction of EEG Signal upon BCI Systems Based on Steady-State Visual Evoked Potentials Using the Ant Colony Optimization Algorithm

“…Recently, SI algorithms have been used in biomedical data processing of biomedical data such as EEG and electrocardiogram (ECG) [19][20][21][22]. For instance, Cheng et al The first proposed algorithm within the ACO metaheuristic was the Ant System (AS) to solve the Travel Agent Problem (TSP) problem in which we search for the optimal route between a set of cities that the traveler must visit only once and return to the starting point.…”

A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders

“…where δ is a constant and J k is the fitness value of selected feature set by ant k [77]. By using this rule, the pheromone level of the features with highest fitness value will increase frequently, which will make that set more inclined to be selected during subsequent iterations by the ants.…”

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

“…They have performed automated classification through ant colony approach (ACO_DTree algorithm) and the Group of Adaptive Models Evolution inductive models. Khushaba et al [151] proposed a new feature extraction method which utilizes ant colony optimization in the selection of wavelet packet transform (WPT) and adopted in classifying bio-medical signals.…”

“…Results show that the proposed method achieves a maximum accuracy of 83 %. Again, Khushaba et al [151] investigated the use of a combination of ACO and differential evolution called ANTDE for feature selection. They compare ANTDE, GA and BPSO, and reported that ANTDE's outperforms GA and BPSO due to the use of a mutual information based heuristic measure.…”