ECG Signal Processing for Respiratory and Autonomic Modulation in Atrial Fibrillation and Environmental Exposure
(2025)- Abstract
- Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risk of stroke, heart failure, and mortality. One of the systems which AF is influenced by is the autonomic nervous system (ANS), which regulates involuntary functions such as heart rate. Environmental stressors, such as air pollution, may also affect these physiological systems and contribute to cardiovascular morbidity. The electrocardiogram (ECG) provides a non-invasive tool to study these influences, but the irregular rhythm of AF poses challenges for conventional respiratory and autonomic analysis. The overall aim of this thesis was to develop ECG-based methods for assessing respiratory and autonomic modulation during AF, and to... (More)
- Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risk of stroke, heart failure, and mortality. One of the systems which AF is influenced by is the autonomic nervous system (ANS), which regulates involuntary functions such as heart rate. Environmental stressors, such as air pollution, may also affect these physiological systems and contribute to cardiovascular morbidity. The electrocardiogram (ECG) provides a non-invasive tool to study these influences, but the irregular rhythm of AF poses challenges for conventional respiratory and autonomic analysis. The overall aim of this thesis was to develop ECG-based methods for assessing respiratory and autonomic modulation during AF, and to extend ECG analysis to explore autonomic responses to environmental stressors using different methodological approaches. The first aim was to develop and validate signal processing methods for modeling and tracking respiratory f-wave frequency modulation during AF, as a potential noninvasive marker of autonomic nervous system activity in the atria and of AF progression. This aim is addressed in Papers I and II, which introduced novel algorithms for estimating respiratory f-wave frequency modulation from the ECG, and demonstrated that such modulation can be robustly quantified from the ECG, with results suggesting a contribution from parasympathetic activity. The second aim was to examine the influence of autonomic modulation on atrial activity during AF by analyzing respiratory f-wave frequency modulation during tilt-test, and to explore underlying mechanisms using computational modeling. This aim is addressed in Paper III, which combined clinical tilt-table testing in persistent AF patients with biatrial computer simulations to investigate sympathetic and parasympathetic contributions to observed modulation patterns. Results suggested that sympathetic activity primarily influenced the mean fibrillatory rate, while parasympathetic activity appeared to modulate respiration-related variations as a secondary effect rather than as an independent driver. The third aim was to apply ECG-based analysis to assess respiratory and autonomic responses in healthy individuals exposed to hydrotreated vegetable oil (HVO) emissions, using methods distinct from those developed for AF. This aim is addressed in Paper IV, which employed ECG analysis techniques in a controlled human exposure study and indicated that short-term exposure to HVO exhaust did not lead to significant alterations in autonomic or respiratory regulation. In summary, this thesis presents new methods for extracting respiratory and autonomic information from ECGs recorded under challenging conditions, including AF and environmental exposure scenarios. These tools extend the capabilities of ECG analysis, offering potential applications in clinical AF management, environmental health research, and personalized medicine. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/92e7290e-b706-4d2e-8b7d-e07cef7c0323
- author
- Abdollahpur, Mostafa LU
- supervisor
-
- Frida Sandberg LU
- Pyotr Platonov LU
- opponent
-
- Dr. Meo, Marianna, Boston Scientific Corp., The Netherlands.
- organization
- publishing date
- 2025-10-09
- type
- Thesis
- publication status
- published
- subject
- pages
- 180 pages
- publisher
- Department of Biomedical Engineering, Lund university
- defense location
- Lecture Hall E:1406, building E, Ole Römers väg 3, Faculty of Engineering LTH, Lund University, Lund.
- defense date
- 2025-11-10 09:00:00
- ISBN
- 978-91-8104-726-4
- 978-91-8104-727-1
- language
- English
- LU publication?
- yes
- id
- 92e7290e-b706-4d2e-8b7d-e07cef7c0323
- date added to LUP
- 2025-10-09 16:31:30
- date last changed
- 2025-10-16 09:18:41
@phdthesis{92e7290e-b706-4d2e-8b7d-e07cef7c0323,
abstract = {{Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risk of stroke, heart failure, and mortality. One of the systems which AF is influenced by is the autonomic nervous system (ANS), which regulates involuntary functions such as heart rate. Environmental stressors, such as air pollution, may also affect these physiological systems and contribute to cardiovascular morbidity. The electrocardiogram (ECG) provides a non-invasive tool to study these influences, but the irregular rhythm of AF poses challenges for conventional respiratory and autonomic analysis. The overall aim of this thesis was to develop ECG-based methods for assessing respiratory and autonomic modulation during AF, and to extend ECG analysis to explore autonomic responses to environmental stressors using different methodological approaches. The first aim was to develop and validate signal processing methods for modeling and tracking respiratory f-wave frequency modulation during AF, as a potential noninvasive marker of autonomic nervous system activity in the atria and of AF progression. This aim is addressed in Papers I and II, which introduced novel algorithms for estimating respiratory f-wave frequency modulation from the ECG, and demonstrated that such modulation can be robustly quantified from the ECG, with results suggesting a contribution from parasympathetic activity. The second aim was to examine the influence of autonomic modulation on atrial activity during AF by analyzing respiratory f-wave frequency modulation during tilt-test, and to explore underlying mechanisms using computational modeling. This aim is addressed in Paper III, which combined clinical tilt-table testing in persistent AF patients with biatrial computer simulations to investigate sympathetic and parasympathetic contributions to observed modulation patterns. Results suggested that sympathetic activity primarily influenced the mean fibrillatory rate, while parasympathetic activity appeared to modulate respiration-related variations as a secondary effect rather than as an independent driver. The third aim was to apply ECG-based analysis to assess respiratory and autonomic responses in healthy individuals exposed to hydrotreated vegetable oil (HVO) emissions, using methods distinct from those developed for AF. This aim is addressed in Paper IV, which employed ECG analysis techniques in a controlled human exposure study and indicated that short-term exposure to HVO exhaust did not lead to significant alterations in autonomic or respiratory regulation. In summary, this thesis presents new methods for extracting respiratory and autonomic information from ECGs recorded under challenging conditions, including AF and environmental exposure scenarios. These tools extend the capabilities of ECG analysis, offering potential applications in clinical AF management, environmental health research, and personalized medicine.}},
author = {{Abdollahpur, Mostafa}},
isbn = {{978-91-8104-726-4}},
language = {{eng}},
month = {{10}},
publisher = {{Department of Biomedical Engineering, Lund university}},
school = {{Lund University}},
title = {{ECG Signal Processing for Respiratory and Autonomic Modulation in Atrial Fibrillation and Environmental Exposure}},
url = {{https://lup.lub.lu.se/search/files/229551601/Mostafa_Abdollahpur_PhD_Thesis_Open.pdf}},
year = {{2025}},
}