Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

[2410.10758] Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2410.10758 (eess) [Submitted on 14 Oct 2024 ( v1 ), last revised 10 Feb 2025 (this version, v4)] Title: Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix Authors: Seungwoo Han View a PDF of the paper titled Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix, by Seungwoo Han View PDF Abstract: With the advancements in graph neural network, there has been increasing interest in applying this network to ECG signal analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted features and applied a graph neural network to classify arrhythmias. The proposed model was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that this method can be considered an approach for arrhythmia classification. Comments: Corrected typos Subjects: Signal Processing (eess.SP) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2410.10758 [eess.SP] (or arXiv:2410.10758v4 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2410.10758 Focus to learn more arXiv-issued DOI via DataCite Journal reference: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 6400-6402, 2024 Related DOI : https://doi.org/10.1109/BIBM62325.2024.10822569 Focus to learn more DOI(s) linking to related resources Submission history From: Seungwoo Han [ view email ] [v1] Mon, 14 Oct 2024 17:38:37 UTC (482 KB) [v2] Tue, 5 Nov 2024 17:00:25 UTC (485 KB) [v3] Wed, 13 Nov 2024 05:11:05 UTC (488 KB) [v4] Mon, 10 Feb 2025 14:40:49 UTC (494 KB) Full-text links: Access Paper: View a PDF of the paper titled Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix, by Seungwoo Han View PDF view license Current browse context: eess.SP < prev | next > new | recent | 2024-10 Change to browse by: cs cs.AI eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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