Ant colony inspired metaheuristics in biological signal processing - Hybrid ant colony and evolutionary approach

Nature inspired metaheuristics have interesting stochastic properties which make them suitable for use in data mining, data clustering and other application areas, because they often produce more robust solutions. This paper presents an application of clustering method inspired by the behavior of real ants in the nature to biomedical signal processing. The main aim of our 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 targets the speed-up and the increase in objectivity of identification of important classes and may be used for online classification, so it can be used as a hint in the expert classification process. We have obtained significant results in electrocardiogram and electroencephalogram recordings, which justify the use of such kind of methods.