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A graph signal processing framework for atrial activity extraction. / Sun, Miao; Isufi, Elvin; De Groot, Natasja M.S.; Hendriks, Richard C.

EUSIPCO 2019 - 27th European Signal Processing Conference. Vol. 2019-September European Signal Processing Conference, EUSIPCO, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Sun, M, Isufi, E, De Groot, NMS & Hendriks, RC 2019, A graph signal processing framework for atrial activity extraction. in EUSIPCO 2019 - 27th European Signal Processing Conference. vol. 2019-September, European Signal Processing Conference, EUSIPCO, 27th European Signal Processing Conference, EUSIPCO 2019, A Coruna, Spain, 2/09/19. https://doi.org/10.23919/EUSIPCO.2019.8902778

APA

Sun, M., Isufi, E., De Groot, N. M. S., & Hendriks, R. C. (2019). A graph signal processing framework for atrial activity extraction. In EUSIPCO 2019 - 27th European Signal Processing Conference (Vol. 2019-September). European Signal Processing Conference, EUSIPCO. https://doi.org/10.23919/EUSIPCO.2019.8902778

Vancouver

Sun M, Isufi E, De Groot NMS, Hendriks RC. A graph signal processing framework for atrial activity extraction. In EUSIPCO 2019 - 27th European Signal Processing Conference. Vol. 2019-September. European Signal Processing Conference, EUSIPCO. 2019 https://doi.org/10.23919/EUSIPCO.2019.8902778

Author

Sun, Miao ; Isufi, Elvin ; De Groot, Natasja M.S. ; Hendriks, Richard C. / A graph signal processing framework for atrial activity extraction. EUSIPCO 2019 - 27th European Signal Processing Conference. Vol. 2019-September European Signal Processing Conference, EUSIPCO, 2019.

BibTeX

@inproceedings{644c7614a6bd48e298be0038f31f0c5f,
title = "A graph signal processing framework for atrial activity extraction",
abstract = "Atrial fibrillation (AF) is a common cardiac arrhythmia and its mechanisms are not yet fully understood. Analyzing atrial epicardial electrograms (EGMs) is important to understand the mechanisms underlying AF. However, when measuring the atrial activity (AA), the electrogram is commonly distorted by the far-field ventricular activity (VA). During sinus rhythm, the AA and the VA are separated in time. However, the VA often overlaps with the AA in both time and frequency domain during AF, complicating proper analysis of the AA. Unlike traditional methods, this work explores graph signal processing (GSP) tools for AA extraction in EGMs. Since EGMs are time-varying and non-stationary, we put forward the joint graph and short-time Fourier transform to analyze the graph signal along both time and vertices. It is found that the temporal frequency components of the AA and the VA exhibit different levels of spatial variation over the graph in the joint domain. Subsequently, we exploit these findings to propose a novel algorithm for extracting the AA based on graph smoothness. Experimental results on synthetic and real data show that the smoothness analysis of the EGMs over the atrial area enables us to better extract the AA.",
keywords = "Atrial activity extraction, Atrial fibrillation, Graph smoothness, Graph-time signal processing",
author = "Miao Sun and Elvin Isufi and {De Groot}, {Natasja M.S.} and Hendriks, {Richard C.}",
year = "2019",
month = "9",
day = "1",
doi = "10.23919/EUSIPCO.2019.8902778",
language = "English",
volume = "2019-September",
booktitle = "EUSIPCO 2019 - 27th European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",

}

RIS

TY - GEN

T1 - A graph signal processing framework for atrial activity extraction

AU - Sun, Miao

AU - Isufi, Elvin

AU - De Groot, Natasja M.S.

AU - Hendriks, Richard C.

PY - 2019/9/1

Y1 - 2019/9/1

N2 - Atrial fibrillation (AF) is a common cardiac arrhythmia and its mechanisms are not yet fully understood. Analyzing atrial epicardial electrograms (EGMs) is important to understand the mechanisms underlying AF. However, when measuring the atrial activity (AA), the electrogram is commonly distorted by the far-field ventricular activity (VA). During sinus rhythm, the AA and the VA are separated in time. However, the VA often overlaps with the AA in both time and frequency domain during AF, complicating proper analysis of the AA. Unlike traditional methods, this work explores graph signal processing (GSP) tools for AA extraction in EGMs. Since EGMs are time-varying and non-stationary, we put forward the joint graph and short-time Fourier transform to analyze the graph signal along both time and vertices. It is found that the temporal frequency components of the AA and the VA exhibit different levels of spatial variation over the graph in the joint domain. Subsequently, we exploit these findings to propose a novel algorithm for extracting the AA based on graph smoothness. Experimental results on synthetic and real data show that the smoothness analysis of the EGMs over the atrial area enables us to better extract the AA.

AB - Atrial fibrillation (AF) is a common cardiac arrhythmia and its mechanisms are not yet fully understood. Analyzing atrial epicardial electrograms (EGMs) is important to understand the mechanisms underlying AF. However, when measuring the atrial activity (AA), the electrogram is commonly distorted by the far-field ventricular activity (VA). During sinus rhythm, the AA and the VA are separated in time. However, the VA often overlaps with the AA in both time and frequency domain during AF, complicating proper analysis of the AA. Unlike traditional methods, this work explores graph signal processing (GSP) tools for AA extraction in EGMs. Since EGMs are time-varying and non-stationary, we put forward the joint graph and short-time Fourier transform to analyze the graph signal along both time and vertices. It is found that the temporal frequency components of the AA and the VA exhibit different levels of spatial variation over the graph in the joint domain. Subsequently, we exploit these findings to propose a novel algorithm for extracting the AA based on graph smoothness. Experimental results on synthetic and real data show that the smoothness analysis of the EGMs over the atrial area enables us to better extract the AA.

KW - Atrial activity extraction

KW - Atrial fibrillation

KW - Graph smoothness

KW - Graph-time signal processing

UR - http://www.scopus.com/inward/record.url?scp=85075606725&partnerID=8YFLogxK

U2 - 10.23919/EUSIPCO.2019.8902778

DO - 10.23919/EUSIPCO.2019.8902778

M3 - Conference contribution

VL - 2019-September

BT - EUSIPCO 2019 - 27th European Signal Processing Conference

PB - European Signal Processing Conference, EUSIPCO

ER -

ID: 67439320