Conference Proceeding
Refine
Year of publication
Document Type
- Conference Proceeding (1016) (remove)
Language
- English (1016) (remove)
Has Fulltext
- no (1016) (remove)
Keywords
- Enterprise Architecture (5)
- Energy storage (4)
- Gamification (4)
- Natural language processing (4)
- Power plants (4)
- hydrogen (4)
- solar sail (4)
- Associated liquids (3)
- Concentrated solar power (3)
- Hybrid energy system (3)
- MASCOT (3)
- Out-of-plane load (3)
- earthquakes (3)
- Additive manufacturing (2)
- Adjacent buildings (2)
- Case Study (2)
- Clustering (2)
- Deep learning (2)
- Digital Twin (2)
- Diversity (2)
Institute
- Fachbereich Elektrotechnik und Informationstechnik (224)
- Fachbereich Luft- und Raumfahrttechnik (171)
- Fachbereich Energietechnik (158)
- Fachbereich Medizintechnik und Technomathematik (131)
- IfB - Institut für Bioengineering (109)
- Solar-Institut Jülich (108)
- Fachbereich Maschinenbau und Mechatronik (98)
- Fachbereich Bauingenieurwesen (70)
- ECSM European Center for Sustainable Mobility (50)
- Fachbereich Wirtschaftswissenschaften (42)
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (42)
- INB - Institut für Nano- und Biotechnologien (33)
- Fachbereich Chemie und Biotechnologie (23)
- Kommission für Forschung und Entwicklung (16)
- Nowum-Energy (11)
- Fachbereich Architektur (7)
- Fachbereich Gestaltung (3)
- Institut fuer Angewandte Polymerchemie (2)
- ZHQ - Bereich Hochschuldidaktik und Evaluation (2)
- Arbeitsstelle fuer Hochschuldidaktik und Studienberatung (1)
- Digitalisierung in Studium & Lehre (1)
- Freshman Institute (1)
- IaAM - Institut für angewandte Automation und Mechatronik (1)
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.