{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/accelerated-sample-accurate-r-peak-detectors","title":"Accelerated Sample-Accurate R-Peak Detectors Based on Visibility Graphs","arxiv_id":null,"date":"2023-09-04","proceeding":"European Signal Processing Conference 2023 9","authors":["Jonas Emrich","Taulant Koka","Sebastian Wirth","Michael Muma"],"abstract":"The effective detection and accurate clinical diagnosis of cardiac conditions strongly relies on the correct localization of R-peaks in the electrocardiogram (ECG). Recently, demand for sample-accurate R-peak detection, which is essential to precisely reveal vital features, such as heart rate variability and pulse transit time, has increased. Therefore, we propose two novel sample-accurate visibility-graph-based R-peak detectors, the FastNVG and the FastWHVG detector. The visibility graph (VG) transformation maps a discrete signal into a graph by representing sampling locations as nodes and establishing edges between mutually visible samples. However, processing large-scale clinical ECG data urgently demands further acceleration of VG-based algorithms. The proposed methods reduce the required computation time by one order of magnitude and simultaneously decrease the required memory compared to a recently proposed VG-based R-Peak detector. Instead of transforming the entire ECG, the proposed acceleration benefits largely from building the VG based on a subset containing only the samples relevant to R-peak detection. Further acceleration is obtained by adopting the computationally efficient horizontal visibility graph, which has not yet been used for R-peak detection. Numerical experiments and benchmarks on multiple ECG databases demonstrate a significantly superior performance of the proposed VG-based methods compared to popular R-peak detectors.","url_abs":"https://ieeexplore.ieee.org/document/10290007","url_pdf":"https://eurasip.org/Proceedings/Eusipco/Eusipco2023/pdfs/0001090.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"accelerated-sample-accurate-r-peak-detectors","repo_url":"https://github.com/JonasEmrich/vg-beat-detectors","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"heart-rate-variability","task_name":"Heart Rate Variability"},{"task_slug":"qrs-complex-detection","task_name":"QRS Complex Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}