| Title |
Reservoir computing for extraction of low amplitude atrial activity in atrial fibrillation / |
| Authors |
Petrėnas, Andrius ; Marozas, Vaidotas ; Sornmo, Leif ; Lukoševičius, Arūnas |
| ISBN |
9781467320764 |
| eISBN |
9781467320771 |
| Full Text |
|
| Is Part of |
Computing in cardiology 2012 (CinC): September 9-12, 2012, Kraków, Poland.. New York, NY : IEEE, 2012. vol. 39, p. 13-16.. ISSN 0276-6574. eISSN 2325-8853. ISBN 9781467320764. eISBN 9781467320771 |
| Keywords [eng] |
surface electrocardiograms ; heart beat ; electrocardiography ; frequency estimation ; reservoirs |
| Abstract [eng] |
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, non-linear 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-ESN 24.8 +/- 7.3 and P-ABS 34.2 +/- 17.9 mu V (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. |
| Published |
New York, NY : IEEE, 2012 |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2012 |
