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

Selim DİLMAÇ; Zümray ÖLMEZ; Tamer ÖLMEZ

Article Details

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

Authors:

Selim DİLMAÇ | Zümray ÖLMEZ | Tamer ÖLMEZ

Publisher:

TUBITAK

Main Topics:

Applied physics

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 6, pp.2819-2830

Document Type:

Article

Publication Date:

11/2018

Document Language:

English

Document ID (Org No.):

2000734458

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

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.

Article Details

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

Authors: Selim DİLMAÇ | Zümray ÖLMEZ | Tamer ÖLMEZ

Publisher: TUBITAK

Main Topics: Applied physics

Journal: Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 6, pp.2819-2830

Document Type: Article

Publication Date: 11/2018

Document Language: English

Document ID (Org No.): 2000734458

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

Selim DİLMAÇ | Zümray ÖLMEZ | Tamer ÖLMEZ

Publisher:

TUBITAK

Main Topics:

Applied physics

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 6, pp.2819-2830

Document Type:

Article

Publication Date:

11/2018

Document Language:

English

Document ID (Org No.):

2000734458

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632