Signal quality assessment of f-waves in atrial fibrillation
(2017) 44th Computing in Cardiology, CinC 2017 In Computing in Cardiology- Abstract
Ambulatory ECG recordings are frequently corrupted by artifacts caused by, e.g., muscle activity or moving electrodes, which complicates the analysis of f-waves and motivates signal quality assessment to improve the reliability of f-wave analysis. Although many methods have been developed for assessing the quality of ECG signals in general, no method deals specifically with f-waves. This study proposes a novel signal quality index (SQI), using a modelbased approach for assessment of f-wave signal quality. To evaluate the performance of the SQI, 189 5-s recordings of f-waves from AF patients are studied, as is the same number of recordings with motion artifacts and electrode movements taken from the MIT-BIH Noise Stress Test Database.... (More)
Ambulatory ECG recordings are frequently corrupted by artifacts caused by, e.g., muscle activity or moving electrodes, which complicates the analysis of f-waves and motivates signal quality assessment to improve the reliability of f-wave analysis. Although many methods have been developed for assessing the quality of ECG signals in general, no method deals specifically with f-waves. This study proposes a novel signal quality index (SQI), using a modelbased approach for assessment of f-wave signal quality. To evaluate the performance of the SQI, 189 5-s recordings of f-waves from AF patients are studied, as is the same number of recordings with motion artifacts and electrode movements taken from the MIT-BIH Noise Stress Test Database. The signal quality index is capable of discriminating between f-waves and noisy recordings with an accuracy of 98%. The results suggest that the proposed signal quality index correctly identifies noisy recordings, and can be used to improve the reliability of f-wave analysis.
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- author
- Henriksson, Mikael LU ; Petrenas, Andrius ; Marozas, Vaidotas ; Sandberg, Frida LU and Sörnmo, Leif LU
- organization
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2017 Computing in Cardiology (CinC)
- series title
- Computing in Cardiology
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 44th Computing in Cardiology, CinC 2017
- conference location
- Rennes, France
- conference dates
- 2017-09-24 - 2017-09-27
- external identifiers
-
- scopus:85045113529
- ISSN
- 2325-8861
- ISBN
- 978-1-5386-6630-2
- 978-1-5386-4555-0
- DOI
- 10.22489/CinC.2017.051-153
- project
- Modelling and Quality Assessment of Atrial Fibrillatory Waves
- language
- English
- LU publication?
- yes
- id
- a6c74b16-3abd-4753-b653-adad0a8c2150
- date added to LUP
- 2018-04-17 08:28:42
- date last changed
- 2024-06-15 08:43:20
@inproceedings{a6c74b16-3abd-4753-b653-adad0a8c2150, abstract = {{<p>Ambulatory ECG recordings are frequently corrupted by artifacts caused by, e.g., muscle activity or moving electrodes, which complicates the analysis of f-waves and motivates signal quality assessment to improve the reliability of f-wave analysis. Although many methods have been developed for assessing the quality of ECG signals in general, no method deals specifically with f-waves. This study proposes a novel signal quality index (SQI), using a modelbased approach for assessment of f-wave signal quality. To evaluate the performance of the SQI, 189 5-s recordings of f-waves from AF patients are studied, as is the same number of recordings with motion artifacts and electrode movements taken from the MIT-BIH Noise Stress Test Database. The signal quality index is capable of discriminating between f-waves and noisy recordings with an accuracy of 98%. The results suggest that the proposed signal quality index correctly identifies noisy recordings, and can be used to improve the reliability of f-wave analysis.</p>}}, author = {{Henriksson, Mikael and Petrenas, Andrius and Marozas, Vaidotas and Sandberg, Frida and Sörnmo, Leif}}, booktitle = {{2017 Computing in Cardiology (CinC)}}, isbn = {{978-1-5386-6630-2}}, issn = {{2325-8861}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{Computing in Cardiology}}, title = {{Signal quality assessment of f-waves in atrial fibrillation}}, url = {{http://dx.doi.org/10.22489/CinC.2017.051-153}}, doi = {{10.22489/CinC.2017.051-153}}, year = {{2017}}, }