@inproceedings{AlhaskirTschescheLinkeetal.2023,
  author    = {Alhaskir, Mohamed and Tschesche, Matteo and Linke, Florian and Schriewer, Elisabeth and Weber, Yvonne and Wolking, Stefan and R{\"o}hrig, Rainer and Koch, Henner and Kutafina, Ekaterina},
  title     = {ECG matching: an approach to synchronize ECG datasets for data quality comparisons},
  series = {German Medical Data Sciences 2023 - Science. Close to People.},
  volume    = {307},
  booktitle = {German Medical Data Sciences 2023 - Science. Close to People.},
  editor    = {R{\"o}hrig, Rainer and Grabe, Niels and Haag, Martin and H{\"u}bner, Ursula and Sax, Ulrich and Schmidt, Carsten Oliver and Sedlmayr, Martin and Zapf, Antonia},
  publisher = {IOS Press},
  isbn      = {978-1-64368-428-4 (Print)},
  doi       = {10.3233/SHTI230718},
  pages     = {225 -- 232},
  year      = {2023},
  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.},
  language  = {en}
}