A new QRS detection method using wavelets and artificial neural networks

Berdakh Abibullaev, Hee Don Seo

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)


We present a new method for detection and classification of QRS complexes in ECG signals using continuous wavelets and neural networks. Our wavelet method consists of four wavelet basis functions that are suitable in detection of QRS complexes within different QRS morphologies in the signal and thresholding technique for denoising and feature extraction. The results demonstrate that the proposed method is not only efficient for normal ECG signal analysis but also for various types of arrhythmic cardiac signals embedded in noise. For the classification stage, a feedforward neural network was trained with standard backpropagation algorithm. The classifier input features consisted of compact wavelet coefficients of QRS complexes that resulted in higher classification rates. We demonstrate the efficiency of our method with the average accuracy 97.2% in classification of normal and abnormal QRS complexes.

Original languageEnglish
Pages (from-to)683-691
Number of pages9
JournalJournal of Medical Systems
Issue number4
Publication statusPublished - Aug 2011


  • Arrhythmia detection
  • Artificial neural networks
  • ECG
  • QRS complex
  • Wavelet transforms

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management

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