Evaluation of a New Heart Beat Classification Method Based on ABC Algorithm, Comparison with GA, PSO and ACO Classifiers

Abstract

In this paper, we proposed a new method modified artificial bee colony (MABC) algorithm and it is applied to ECG signal analysis for heart beat classification. MITBIH database ECG data is used. In this dataset, MABC algorithm can reach high classification success rate, even with the low values of colony size and other control parameters such as MCN and limit. The classification success rate result of MABC is compared with results of three other classifiers: GA, PSO and ACO. In classification problem, choosing distinctive features has important effect to get a high classification success rate. By using the right features on analysed dataset, high system classification success rate (98.73%) is achieved by MABC, similar to other compared classifiers. MABC and ACO has high sensitivity for all beat types while GA and PSO have lower classification success rates for some beat types.