Heartbeat type classification with optimized feature vectors

Özal Yıldırım*, Ulas Baran Baloglu

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)
150 Downloads (Pure)


In this study, a feature vector optimization based method has been proposed for classification of the heartbeat types. Electrocardiogram (ECG) signals of five different heartbeat type were used for this aim. Firstly, wavelet transform (WT) method were applied on these ECG signals to generate all feature vectors. Optimizing these feature vectors is provided by performing particle swarm optimization (PSO), genetic search, best first, greedy stepwise and multi objective evoluationary algorithms on these vectors. These optimized feature vectors are later applied to the classifier inputs for performance evaluation. A comprehensive assessment was presented for the determination of optimized feature vectors for ECG signals and best-performing classifier for these optimized feature vectors was determined.

Original languageEnglish
Pages (from-to)170-175
Number of pages6
JournalInternational Journal of Optimization and Control: Theories and Applications
Issue number2
Early online date12 Apr 2018
Publication statusE-pub ahead of print - 12 Apr 2018


  • Classification
  • ECG signals
  • Feature optimization
  • Feature vectors
  • Machine learning


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