An Echo State Neural Network for QRST Cancellation During Atrial Fibrillation

Research output: Contribution to journalArticle


A novel method for QRST cancellation during atrial fibrillation (AF) is introduced for use in recordings with two or more leads. The method is based on an echo state neural network which estimates the time-varying, nonlinear transfer function between two leads, one lead with atrial activity and another lead without, for the purpose of canceling ventricular activity. The network has different sets of weights that define the input, hidden, and output layers, of which only the output set is adapted for every new sample to be processed. The performance is evaluated on ECG signals, with simulated f-waves added, by determining the root mean square error between the true f-wave signal and the estimated signal, as well as by evaluating the dominant AF frequency. When compared to average beat subtraction (ABS), being the most widely used method for QRST cancellation, the performance is found to be significantly better with an error reduction factor of 0.24-0.43, depending on f-wave amplitude. The estimates of dominant AF frequency are considerably more accurate for all f-wave amplitudes than the AF estimates based on ABS. The novel method is particularly well suited for implementation in mobile health systems where monitoring of AF during extended time periods is of interest.


  • Andrius Petrenas
  • Vaidotas Marozas
  • Leif Sörnmo
  • Arunas Lukosevicius
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Engineering


  • Atrial fibrillation (AF), average beat substraction (ABS), echo state, neural network, f-wave modeling, QRST cancellation, reservoir computing
Original languageEnglish
Pages (from-to)2950-2957
JournalIEEE Transactions on Biomedical Engineering
Issue number10
Publication statusPublished - 2012
Publication categoryResearch