TY  - CHAP
A1  - Alhaskir, Mohamed
A1  - Tschesche, Matteo
A1  - Linke, Florian
A1  - Schriewer, Elisabeth
A1  - Weber, Yvonne
A1  - Wolking, Stefan
A1  - Röhrig, Rainer
A1  - Koch, Henner
A1  - Kutafina, Ekaterina
ED  - Röhrig, Rainer
ED  - Grabe, Niels
ED  - Haag, Martin
ED  - Hübner, Ursula
ED  - Sax, Ulrich
ED  - Schmidt, Carsten Oliver
ED  - Sedlmayr, Martin
ED  - Zapf, Antonia
T1  - ECG matching: an approach to synchronize ECG datasets for data quality comparisons
T2  - German Medical Data Sciences 2023 – Science. Close to People.
N2  - 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.
KW  - Electrocardiography
KW  - Wearable electronic device
KW  - Sensors comparison
KW  - Time-series synchronization
Y1  - 2023
SN  - 978-1-64368-428-4 (Print)
SN  - 978-1-64368-429-1 (Online)
U6  - http://dx.doi.org/10.3233/SHTI230718
N1  - 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
VL  - 307
SP  - 225
EP  - 232
PB  - IOS Press
ER  - 
TY  - CHAP
A1  - Arndt, Tobias
A1  - Conzen, Max
A1  - Elsen, Ingo
A1  - Ferrein, Alexander
A1  - Galla, Oskar
A1  - Köse, Hakan
A1  - Schiffer, Stefan
A1  - Tschesche, Matteo
T1  - Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers
T2  - PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments
N2  - This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.
KW  - Datasets
KW  - Neural networks
KW  - Anomaly detection
KW  - Quality control
KW  - Process optimization
Y1  - 2023
SN  - 9798400700699
U6  - http://dx.doi.org/10.1145/3594806.3596558
N1  - PETRA '23: Proceedings of the 16th International Conference on Pervasive Technologies Related to Assistive Environments, Corfu Greece, July 5 - 7, 2023.
SP  - 535
EP  - 542
PB  - ACM
ER  -