Investigating Respiratory Rate Estimation during Paroxysmal Atrial Fibrillation Using an Improved ECG Simulation Model
(2020) 2020 Computing in Cardiology, CinC 2020 In Computing in Cardiology 2020-September.- Abstract
The present study addresses the problem of respiratory rate estimation from ECG-derived respiration (EDR) signals during paroxysmal atrial fibrillation (AF). Novel signal-to-noise ratios between various components of the ECG including the influence of respiration, measured by QRS ensemble variance, the amplitude of fibrillatory waves (f-waves), and the QRS amplitude are introduced to characterize EDR performance. Using an improved ECG simulation model accounting for morphological variation induced by respiration, the results show that 1. the error in estimating the respiratory rate increases as a function of the time spent in AF, 2. the leads farthest away from the atria, i.e., V_{4}, V_{5}, V_{6}, exhibit the best performance due to... (More)
The present study addresses the problem of respiratory rate estimation from ECG-derived respiration (EDR) signals during paroxysmal atrial fibrillation (AF). Novel signal-to-noise ratios between various components of the ECG including the influence of respiration, measured by QRS ensemble variance, the amplitude of fibrillatory waves (f-waves), and the QRS amplitude are introduced to characterize EDR performance. Using an improved ECG simulation model accounting for morphological variation induced by respiration, the results show that 1. the error in estimating the respiratory rate increases as a function of the time spent in AF, 2. the leads farthest away from the atria, i.e., V_{4}, V_{5}, V_{6}, exhibit the best performance due to lower f-wave amplitudes, 3. lower errors in leads with similar f-wave amplitude are due to a more pronounced respiratory influence, and 4. the respiratory influence is higher in V_{2}, V_{3}, and V_{4} compared to other precordial leads.
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- author
- Kontaxis, Spyridon ; Martin-Yebra, Alba LU ; Petrenas, Andrius ; Marozas, Vaidotas ; Bailon, Raquel ; Laguna, Pablo and Sornmo, Leif LU
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2020 Computing in Cardiology, CinC 2020
- series title
- Computing in Cardiology
- volume
- 2020-September
- article number
- 9344239
- publisher
- IEEE Computer Society
- conference name
- 2020 Computing in Cardiology, CinC 2020
- conference location
- Rimini, Italy
- conference dates
- 2020-09-13 - 2020-09-16
- external identifiers
-
- scopus:85100944749
- ISSN
- 2325-8861
- 2325-887X
- ISBN
- 9781728173825
- DOI
- 10.22489/CinC.2020.239
- language
- English
- LU publication?
- yes
- id
- e4c1f9a5-47f3-46ce-b825-6559dc9f2d24
- date added to LUP
- 2021-03-05 11:18:07
- date last changed
- 2025-04-04 15:26:19
@inproceedings{e4c1f9a5-47f3-46ce-b825-6559dc9f2d24,
abstract = {{<p>The present study addresses the problem of respiratory rate estimation from ECG-derived respiration (EDR) signals during paroxysmal atrial fibrillation (AF). Novel signal-to-noise ratios between various components of the ECG including the influence of respiration, measured by QRS ensemble variance, the amplitude of fibrillatory waves (f-waves), and the QRS amplitude are introduced to characterize EDR performance. Using an improved ECG simulation model accounting for morphological variation induced by respiration, the results show that 1. the error in estimating the respiratory rate increases as a function of the time spent in AF, 2. the leads farthest away from the atria, i.e., V_{4}, V_{5}, V_{6}, exhibit the best performance due to lower f-wave amplitudes, 3. lower errors in leads with similar f-wave amplitude are due to a more pronounced respiratory influence, and 4. the respiratory influence is higher in V_{2}, V_{3}, and V_{4} compared to other precordial leads.</p>}},
author = {{Kontaxis, Spyridon and Martin-Yebra, Alba and Petrenas, Andrius and Marozas, Vaidotas and Bailon, Raquel and Laguna, Pablo and Sornmo, Leif}},
booktitle = {{2020 Computing in Cardiology, CinC 2020}},
isbn = {{9781728173825}},
issn = {{2325-8861}},
language = {{eng}},
publisher = {{IEEE Computer Society}},
series = {{Computing in Cardiology}},
title = {{Investigating Respiratory Rate Estimation during Paroxysmal Atrial Fibrillation Using an Improved ECG Simulation Model}},
url = {{http://dx.doi.org/10.22489/CinC.2020.239}},
doi = {{10.22489/CinC.2020.239}},
volume = {{2020-September}},
year = {{2020}},
}