TU Darmstadt / ULB / TUbiblio

Fast and Sample Accurate R-Peak Detection for Noisy ECG Using Visibility Graphs

Koka, Taulant ; Muma, Michael (2022)
Fast and Sample Accurate R-Peak Detection for Noisy ECG Using Visibility Graphs.
44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Glasgow, United Kingdom (11.-15.07.2022)
doi: 10.1109/EMBC48229.2022.9871266
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

More than a century has passed since Einthoven laid the foundation of modern electrocardiography and in recent years, driven by the advance of wearable and low budget devices, a sample accurate detection of R-peaks in noisy ECG-signals has become increasingly important. To accommodate these demands, we propose a new R-peak detection approach that builds upon the visibility graph transformation, which maps a discrete time series to a graph by expressing each sample as a node and assigning edges between intervisible samples. The proposed method takes advantage of the high connectivity of large, isolated values to weight the original signal so that R-peaks are amplified while other signal components and noise are suppressed. A simple thresholding procedure, such as the widely used one by Pan and Tompkins, is then sufficient to accurately detect the R-peaks. The weights are computed for overlapping segments of equal size and the time complexity is shown to be linear in the number of segments. Finally, the method is benchmarked against existing methods using the same thresholding on a noisy and sample accurate database. The results illustrate the potential of the proposed method, which outperforms common detectors by a significant margin.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2022
Autor(en): Koka, Taulant ; Muma, Michael
Art des Eintrags: Bibliographie
Titel: Fast and Sample Accurate R-Peak Detection for Noisy ECG Using Visibility Graphs
Sprache: Englisch
Publikationsjahr: 8 September 2022
Verlag: IEEE
Buchtitel: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Veranstaltungstitel: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society
Veranstaltungsort: Glasgow, United Kingdom
Veranstaltungsdatum: 11.-15.07.2022
DOI: 10.1109/EMBC48229.2022.9871266
Kurzbeschreibung (Abstract):

More than a century has passed since Einthoven laid the foundation of modern electrocardiography and in recent years, driven by the advance of wearable and low budget devices, a sample accurate detection of R-peaks in noisy ECG-signals has become increasingly important. To accommodate these demands, we propose a new R-peak detection approach that builds upon the visibility graph transformation, which maps a discrete time series to a graph by expressing each sample as a node and assigning edges between intervisible samples. The proposed method takes advantage of the high connectivity of large, isolated values to weight the original signal so that R-peaks are amplified while other signal components and noise are suppressed. A simple thresholding procedure, such as the widely used one by Pan and Tompkins, is then sufficient to accurately detect the R-peaks. The weights are computed for overlapping segments of equal size and the time complexity is shown to be linear in the number of segments. Finally, the method is benchmarked against existing methods using the same thresholding on a noisy and sample accurate database. The results illustrate the potential of the proposed method, which outperforms common detectors by a significant margin.

Freie Schlagworte: emergenCITY, emergenCITY_CPS
ID-Nummer: pmid:36086455
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Robust Data Science
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
Hinterlegungsdatum: 07 Nov 2022 14:32
Letzte Änderung: 02 Feb 2023 09:43
PPN:
Export:
Suche nach Titel in: TUfind oder in Google
Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen