Genetic algorithm based independent component analysis to separate noise from Electrocardiogram signals

A technique is proposed to reduce additive noise from biomedical signals that have high kurtosis values using genetic algorithm (GA). The technique is applied to reduce multiple linear additive noises from electrocardiogram (ECG) signals, which have high kurtosis values due to the presence of R peaks. This GA method uses the basic principles of Independent Component Analysis (ICA) and could also be used to reduce additive noise from other signals that have high kurtosis values. The method is simpler compared to neural learning algorithms and does not require any prior statistical knowledge of the signals. An additional advantage of the method compared to other ICA methods is that only the ECG signal will be extracted thus avoiding extraction of all independent components and manual inspection to determine the ECG signal.