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

Authors

SELİM DİLMAÇ
ZÜMRAY ÖLMEZ
TAMER ÖLMEZ

DOI

10.3906/elk-1712-328

Abstract

In this paper, we proposed a classification method based on a nature-inspired algorithm, i.e., modified artificial bee colony (MABC). This method was applied to electrocardiogram (ECG) heartbeat classification. ECG data was 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 was done by divergence analysis. MABC classification accuracy and heartbeat sensitivity values were compared with the results 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 MABC classifier achieved 97.18 % accuracy on the analyzed dataset, as well as high sensitivity values for heartbeat types.

Keywords

ECG heartbeat, data classification, ABC algorithm, nature-inspired

First Page

2819

Last Page

2830

Recommended Citation

DİLMAÇ, SELİM; ÖLMEZ, ZÜMRAY; and ÖLMEZ, TAMER (2018) "Comparative analysis of MABC with KNN, SOM, and ACO algorithms for ECG heartbeat classification," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 26: No. 6, Article 4. https://doi.org/10.3906/elk-1712-328
Available at: https://journals.tubitak.gov.tr/elektrik/vol26/iss6/4