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An Echo State Neural Network for QRST Cancellation During Atrial Fibrillation

Petrenas, Andrius ; Marozas, Vaidotas ; Sörnmo, Leif LU and Lukosevicius, Arunas (2012) In IEEE Transactions on Biomedical Engineering 59(10). p.2950-2957
Abstract
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... (More)
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. (Less)
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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Atrial fibrillation (AF), average beat substraction (ABS), echo state, neural network, f-wave modeling, QRST cancellation, reservoir computing
in
IEEE Transactions on Biomedical Engineering
volume
59
issue
10
pages
2950 - 2957
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000308989000030
  • scopus:84866527816
  • pmid:22929362
ISSN
1558-2531
DOI
10.1109/TBME.2012.2212895
language
English
LU publication?
yes
id
90ab4fb2-3bae-4f2f-bf37-0d554e7ec1c1 (old id 3189920)
date added to LUP
2016-04-01 14:04:52
date last changed
2020-11-22 04:02:12
@article{90ab4fb2-3bae-4f2f-bf37-0d554e7ec1c1,
  abstract     = {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.},
  author       = {Petrenas, Andrius and Marozas, Vaidotas and Sörnmo, Leif and Lukosevicius, Arunas},
  issn         = {1558-2531},
  language     = {eng},
  number       = {10},
  pages        = {2950--2957},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Biomedical Engineering},
  title        = {An Echo State Neural Network for QRST Cancellation During Atrial Fibrillation},
  url          = {http://dx.doi.org/10.1109/TBME.2012.2212895},
  doi          = {10.1109/TBME.2012.2212895},
  volume       = {59},
  year         = {2012},
}