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

DİLMAÇ S., DOKUR Z., Ölmez T.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, cilt.26, sa.6, ss.2819-2830, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 6
  • Basım Tarihi: 2018
  • Doi Numarası: 10.3906/elk-1712-328
  • Dergi Adı: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.2819-2830
  • Anahtar Kelimeler: ECG heartbeat, data classification, ABC algorithm, nature-inspired, ANT COLONY OPTIMIZATION, BEAT CLASSIFICATION, NEURAL-NETWORK
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.