Comparative analysis of MABC with KNN, SOM, and ACO algorithms for ECG heartbeat classification

In this paper, we proposed a classification method based on a nature-inspired algorithm, i.e., modifiedartificial bee colony (MABC). This method was applied to electrocardiogram (ECG) heartbeat classification. ECG datawas obtained from MITBIH database. Eight different types of heartbeats (N, j, V, F, f, A, a, and R) were analyzed.For a better classification result, both time domain and frequency domain features were used. Feature selection wasdone by divergence analysis. MABC classification accuracy and heartbeat sensitivity values were compared with theresults of other methods. Among other classifiers, k-nearest neighbor (KNN), Kohonen’s self-organizing map (SOM),and ant colony optimization (ACO) were the best performing ones, and therefore their results are presented. The MABCclassifier achieved 97.18% accuracy on the analyzed dataset, as well as high sensitivity values for heartbeat types

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