Reservoir computing for extraction of low amplitude atrial activity in atrial fibrillation
(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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- author
- Petrenas, Andrius ; Marozas, Vaidotas ; Sornmo, Leif LU and Lukosevicius, Arunas
- organization
- publishing date
- 2012-12-01
- 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 < 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}}, }