ECG Matching: An Approach to Synchronize ECG Datasets for Data Quality Comparisons

Studies in health technology and informatics, vol. 307, pp. 225–232

Abstract

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.

Authors 8

1. Mohamed Alhaskir corresponding Aachen Department of Epileptology, Neurology Medical Faculty Institute for Medical Informatics

   RWTH Aachen University

   Affiliation as printed

   Department of Epileptology, Neurology, Medical Faculty, RWTH Aachen University, Aachen, Germany

   Institute for Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany

2. Matteo Tschesche Aachen Institute for Mobile Autonomous Systems and Cognitive Robotics

   FH Aachen

   Affiliation as printed

   Institute for Mobile Autonomous Systems and Cognitive Robotics, FH Aachen University of Applied Sciences, Aachen, Germany

3. Elisabeth Schriewer Aachen Department of Epileptology, Neurology Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Department of Epileptology, Neurology, Medical Faculty, RWTH Aachen University, Aachen, Germany

4. Yvonne G. Weber Aachen Department of Epileptology, Neurology Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Department of Epileptology, Neurology, Medical Faculty, RWTH Aachen University, Aachen, Germany

5. Stefan Wolking Aachen Department of Epileptology, Neurology Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Department of Epileptology, Neurology, Medical Faculty, RWTH Aachen University, Aachen, Germany

6. Rainer Röhrig Aachen Institute for Medical Informatics Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute for Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany

7. Henner Koch Aachen Department of Epileptology, Neurology Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Department of Epileptology, Neurology, Medical Faculty, RWTH Aachen University, Aachen, Germany

8. Ekaterina Kutafina Aachen Institute for Medical Informatics Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute for Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany