A comparative study of the performance of methods for f-wave extraction
(2022) In Physiological Measurement 43(10).- Abstract
Objective. This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods. Approach. We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation... (More)
Objective. This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods. Approach. We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features. Main results. The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.
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- author
- Mihandoost, Sara ; Sörnmo, Leif LU ; Doyen, Matthieu and Oster, Julien
- organization
- publishing date
- 2022-10-28
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- atrial fibrillation, ECG signal processing, f-wave extraction
- in
- Physiological Measurement
- volume
- 43
- issue
- 10
- article number
- 105006
- publisher
- IOP Publishing
- external identifiers
-
- pmid:36179708
- scopus:85140932903
- ISSN
- 0967-3334
- DOI
- 10.1088/1361-6579/ac96ca
- language
- English
- LU publication?
- yes
- id
- 3a281f29-d700-47ea-8ad4-66f3ddd620ba
- date added to LUP
- 2022-12-13 11:54:05
- date last changed
- 2024-04-04 14:06:41
@article{3a281f29-d700-47ea-8ad4-66f3ddd620ba,
abstract = {{<p>Objective. This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods. Approach. We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features. Main results. The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.</p>}},
author = {{Mihandoost, Sara and Sörnmo, Leif and Doyen, Matthieu and Oster, Julien}},
issn = {{0967-3334}},
keywords = {{atrial fibrillation; ECG signal processing; f-wave extraction}},
language = {{eng}},
month = {{10}},
number = {{10}},
publisher = {{IOP Publishing}},
series = {{Physiological Measurement}},
title = {{A comparative study of the performance of methods for f-wave extraction}},
url = {{http://dx.doi.org/10.1088/1361-6579/ac96ca}},
doi = {{10.1088/1361-6579/ac96ca}},
volume = {{43}},
year = {{2022}},
}