ECG matching: an approach to synchronize ECG datasets for data quality comparisons

  • Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.

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Author:Mohamed Alhaskir, Matteo Tschesche, Florian Linke, Elisabeth Schriewer, Yvonne Weber, Stefan Wolking, Rainer Röhrig, Henner Koch, Ekaterina Kutafina
ISBN:978-1-64368-428-4 (Print)
ISBN:978-1-64368-429-1 (Online)
Parent Title (German):German Medical Data Sciences 2023 – Science. Close to People.
Publisher:IOS Press
Editor:Rainer Röhrig, Niels Grabe, Martin Haag, Ursula Hübner, Ulrich Sax, Carsten Oliver Schmidt, Martin Sedlmayr, Antonia Zapf
Document Type:Conference Proceeding
Year of Completion:2023
Date of the Publication (Server):2023/09/18
Tag:Electrocardiography; Sensors comparison; Time-series synchronization; Wearable electronic device
First Page:225
Last Page:232
Proceedings of the 68th. Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023 in Heilbron, Germany

Part of the series: Studies in Health Technology and Informatics
Institutes:FH Aachen / Fachbereich Elektrotechnik und Informationstechnik
collections:Open Access
Verlag / IOS Press
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell