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Reservoir computing for extraction of low amplitude atrial activity in atrial fibrillation

Petrenas, Andrius ; Marozas, Vaidotas ; Sornmo, Leif LU and Lukosevicius, Arunas (2012) 39th Computing in Cardiology Conference, CinC 2012 39. p.13-16
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 (ESN) 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 performance is evaluated on ECG signals, with simulated f-waves of low amplitude added, by determining the root mean square error P 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... (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 (ESN) 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 performance is evaluated on ECG signals, with simulated f-waves of low amplitude added, by determining the root mean square error P 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 equal to mean and standard deviation of PESN 24.8±7.3 and PABS 34.2±17.9 μV (p < 0.001). The novel method is particularly well-suited for implementation in mobile health systems where monitoring of AF during extended time periods is of interest.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2012 Computing in Cardiology
volume
39
article number
6420318
pages
4 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
39th Computing in Cardiology Conference, CinC 2012
conference location
Krakow, Poland
conference dates
2012-09-09 - 2012-09-12
external identifiers
  • scopus:84875660017
ISBN
9781467320740
language
English
LU publication?
yes
id
18069198-9cf7-4c02-aebe-155876d75c81
alternative location
https://ieeexplore.ieee.org/document/6420318
date added to LUP
2019-06-04 15:46:19
date last changed
2022-01-31 21:25:50
@inproceedings{18069198-9cf7-4c02-aebe-155876d75c81,
  abstract     = {{<p>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 (ESN) 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 performance is evaluated on ECG signals, with simulated f-waves of low amplitude added, by determining the root mean square error P 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 equal to mean and standard deviation of P<sub>ESN</sub> 24.8±7.3 and P<sub>ABS</sub> 34.2±17.9 μV (p &lt; 0.001). The novel method is particularly well-suited for implementation in mobile health systems where monitoring of AF during extended time periods is of interest.</p>}},
  author       = {{Petrenas, Andrius and Marozas, Vaidotas and Sornmo, Leif and Lukosevicius, Arunas}},
  booktitle    = {{2012 Computing in Cardiology}},
  isbn         = {{9781467320740}},
  language     = {{eng}},
  month        = {{12}},
  pages        = {{13--16}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Reservoir computing for extraction of low amplitude atrial activity in atrial fibrillation}},
  url          = {{https://ieeexplore.ieee.org/document/6420318}},
  volume       = {{39}},
  year         = {{2012}},
}