ENHANCEMENT OF ECG CLASSIFICATION USING GA AND PSO

Surbhi Bansal, Amritpal Singh

Abstract: ECG signal classification utilizes for different predictions of heart diseases. These ECG signals have to be classified using different frequency bands according to different energy levels for better prediction of features. These signals have to be classified in different five bands P, Q, R, S and T. These sub-bands provide peak information available in different sub-bands. For the classification various approaches have to be implemented for filtration of signal. In the purposed work Adaptive filter has been implemented for the noise reduction from these signals. Classification of the ECG signal has been optimized using Genetic Algorithm and Particle Swarm Optimization approach. These approaches of classification provide better results i.e. 100 and 100 for 106o and 119o respectively for energy levels of ECG signal.

Keywords: ECG, noise reduction, Genetic Algorithm and Particle Swarm Optimization approach

DOI: https://doi.org/10.15623/ijret.2015.0405056