Ant Colony Cooperative Strategy in Electrocardiogram and Electroencephalogram Data Clustering

Cooperation in natural processes is very important feature, which is modeled by many nature-inspired algorithms. Nature inspired metaheuristics have interesting stochastic properties which make them suitable for use in data mining, data clustering and other computationally demanding application areas. It is because they often produce robust solutions in fairly reasonable time. This paper presents an application of clustering method inspired by the behavior of real ants in the nature in biomedical signal processing. The ants cooperatively maintain and evolve a pheromone matrix which is used to select features. The main aim of this study was to design and develop a combination of feature extraction and classification methods for automatic recognition of significant structure in biological signal recordings. The method is targeted towards speeding up and increasing objectivity of identification of important classes and may be used for online classification. Inherent properties of the method make it suitable for analysis of newly incoming data. The method can be also used in the expert classification process. We have obtained significant results in electrocardiogram and electroencephalogram recordings, which justify the use of such method.