Iterative function system and genetic algorithm-based EEG compression

Abstract

A method for EEG compression is proposed, using Iterative Function System (IFS) and Genetic Algorithms (GAs) with elitist model, keeping the quality sufficiently good for clinical purposes. Compression using IFS is usually called fractal compression. The self transformability property of the EEG signals is assumed and is exploited in the fractal compression technique. To ascertain the self transformability of the EEG signal, some isometric transformations have been applied. The technique described here utilizes Genetic Algorithm that decreases the search space for finding the self similarities in the given signal. This article presents theory and implementation of the proposed method. The fidelity of the reconstructed signal obtained by the present compression algorithm has been assessed both qualitatively and quantitatively. The compression ratios, for the EEG signals in various states, are found to be comparable to the other available techniques for EEG compression. In our method at least 85% data reduction has been achieved.