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Signal quality assessment of f-waves in atrial fibrillation

Henriksson, Mikael LU ; Petrenas, Andrius ; Marozas, Vaidotas ; Sandberg, Frida LU and Sörnmo, Leif LU (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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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
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
2025-04-04 13:58:58
@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}},
}