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A subspace projection approach to quantify respiratory variations in the f-wave frequency trend

Background: The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such in...

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Autores principales: Abdollahpur, Mostafa, Engström, Gunnar, Platonov, Pyotr G., Sandberg, Frida
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9527347/
https://www.ncbi.nlm.nih.gov/pubmed/36200057
http://dx.doi.org/10.3389/fphys.2022.976925
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author Abdollahpur, Mostafa
Engström, Gunnar
Platonov, Pyotr G.
Sandberg, Frida
author_facet Abdollahpur, Mostafa
Engström, Gunnar
Platonov, Pyotr G.
Sandberg, Frida
author_sort Abdollahpur, Mostafa
collection PubMed
description Background: The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information. Objective: This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients. Methods: A harmonic model is fitted to the f-wave signal to estimate a high-resolution f-wave frequency trend, and an orthogonal subspace projection approach is employed to quantify variations in the frequency trend that are linearly related to respiration using an ECG-derived respiration signal. The performance of the proposed approach is evaluated and compared to that of a previously proposed bandpass filtering approach using simulated f-wave signals. Further, the proposed approach is applied to analyze ECG data recorded for 5 min during baseline and 1 min deep breathing from 28 AF patients from the Swedish cardiopulmonary bioimage study (SCAPIS). Results: The simulation results show that the estimates of respiratory variations obtained using the proposed approach are more accurate than estimates obtained using the previous approach. Results from the analysis of SCAPIS data show no significant differences between baseline and deep breathing in heart rate (75.5 ± 22.9 vs. 74 ± 22.3) bpm, atrial fibrillation rate (6.93 ± 1.18 vs. 6.94 ± 0.66) Hz and respiratory f-wave frequency variations (0.130 ± 0.042 vs. 0.130 ± 0.034) Hz. However, individual variations are large with changes in heart rate and atrial fibrillatory rate in response to deep breathing ranging from −9% to +5% and −8% to +6%, respectively and there is a weak correlation between changes in heart rate and changes in atrial fibrillatory rate (r = 0.38, p < 0.03). Conclusion: Respiratory induced f-wave frequency variations were observed at baseline and during deep breathing. No significant changes in the magnitude of these variations in response to deep breathing was observed in the present study population.
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spelling pubmed-95273472022-10-04 A subspace projection approach to quantify respiratory variations in the f-wave frequency trend Abdollahpur, Mostafa Engström, Gunnar Platonov, Pyotr G. Sandberg, Frida Front Physiol Physiology Background: The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information. Objective: This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients. Methods: A harmonic model is fitted to the f-wave signal to estimate a high-resolution f-wave frequency trend, and an orthogonal subspace projection approach is employed to quantify variations in the frequency trend that are linearly related to respiration using an ECG-derived respiration signal. The performance of the proposed approach is evaluated and compared to that of a previously proposed bandpass filtering approach using simulated f-wave signals. Further, the proposed approach is applied to analyze ECG data recorded for 5 min during baseline and 1 min deep breathing from 28 AF patients from the Swedish cardiopulmonary bioimage study (SCAPIS). Results: The simulation results show that the estimates of respiratory variations obtained using the proposed approach are more accurate than estimates obtained using the previous approach. Results from the analysis of SCAPIS data show no significant differences between baseline and deep breathing in heart rate (75.5 ± 22.9 vs. 74 ± 22.3) bpm, atrial fibrillation rate (6.93 ± 1.18 vs. 6.94 ± 0.66) Hz and respiratory f-wave frequency variations (0.130 ± 0.042 vs. 0.130 ± 0.034) Hz. However, individual variations are large with changes in heart rate and atrial fibrillatory rate in response to deep breathing ranging from −9% to +5% and −8% to +6%, respectively and there is a weak correlation between changes in heart rate and changes in atrial fibrillatory rate (r = 0.38, p < 0.03). Conclusion: Respiratory induced f-wave frequency variations were observed at baseline and during deep breathing. No significant changes in the magnitude of these variations in response to deep breathing was observed in the present study population. Frontiers Media S.A. 2022-09-19 /pmc/articles/PMC9527347/ /pubmed/36200057 http://dx.doi.org/10.3389/fphys.2022.976925 Text en Copyright © 2022 Abdollahpur, Engström, Platonov and Sandberg. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Abdollahpur, Mostafa
Engström, Gunnar
Platonov, Pyotr G.
Sandberg, Frida
A subspace projection approach to quantify respiratory variations in the f-wave frequency trend
title A subspace projection approach to quantify respiratory variations in the f-wave frequency trend
title_full A subspace projection approach to quantify respiratory variations in the f-wave frequency trend
title_fullStr A subspace projection approach to quantify respiratory variations in the f-wave frequency trend
title_full_unstemmed A subspace projection approach to quantify respiratory variations in the f-wave frequency trend
title_short A subspace projection approach to quantify respiratory variations in the f-wave frequency trend
title_sort subspace projection approach to quantify respiratory variations in the f-wave frequency trend
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9527347/
https://www.ncbi.nlm.nih.gov/pubmed/36200057
http://dx.doi.org/10.3389/fphys.2022.976925
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