Arrhythmia detection—An Enhanced Method Using Gramian Angular Matrix for Deep Learning-Reference-Cited by-同舟云学术

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Arrhythmia detection—An Enhanced Method Using Gramian Angular Matrix for Deep Learning

Published:2023 Issue: Volume: Page:785-798
ISSN:2367-3370
Container-title:Third Congress on Intelligent Systems
language:
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Author:

Krishnan Keerthana,Gandhiraj R.,Panda Manoj Kumar

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-9225-4_57

Reference19 articles.

1. Aje TO (2009) Cardiovascular disease: a global problem extending into the developing world. World J Cardiol 1(1):3. https://doi.org/10.4330/wjc.v1.i1.3

2. Handbook of Second Edition Biomedical Instrumentation

3. Sakib S, Fouda MM, Fadlullah ZM (2021) A rigorous analysis of biomedical edge computing: an arrhythmia classification use-case leveraging deep learning. In: 2020 IEEE international conference on internet of things and intelligence system (IoTaIS), pp 136–141.https://doi.org/10.1109/IoTaIS50849.2021.9359721

4. Hannun AY et al (2019) Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 25(1):65–69. https://doi.org/10.1038/s41591-018-0268-3

5. Nandan KVP, Panda M, Veni S (2020) Handwritten digit recognition using ensemble learning. In: 2020 5th International conference on communication and electronics systems (ICCES), pp 1008–1013. https://doi.org/10.1109/ICCES48766.2020.9137933

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1. Low intricate digital twin method to predict cardiac arrhythmia;2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME);2023-07-19

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