Open Access
Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
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Article Number | 01149 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/matecconf/202439201149 | |
Published online | 18 March 2024 |
- Yesudasu Paila, Ravi Raja A, N S P Revathi Nuvvula, R L Durga Prasad Pandi, Pujitha Kodali, Siva Reddy Vanga, “Detection and Analysis of Cardiac Arrhythmias from Heartbeat Classification” in 2023 ICEEICT | 979-8-3503-9763-5/23/$31.00 ©2023 IEEE | DOI: 10.1109/ICEEICT56924.2023.10156983. [Google Scholar]
- Amenah Alwan Salman, Abdullahi IBARAHIM, “ Detection and Analysis of Cardiac Arrhythmias from Heartbeat Classification” in 022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) | 978-1-6654-7013-1/22/$31.00 ©2022 IEEE | DOI: 10.1109/ISMSIT56059.2022.9932742. [Google Scholar]
- M. U. Zahid et al., “Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network,” in IEEE Transactions on Biomedical Engineering, vol. 69, no. 1, pp. 119-128, Jan. 2022, doi: 109/TBME.2021.3088218. [CrossRef] [Google Scholar]
- Dr. Subhashini1, Mareddy Sushma, Basampally Yeshwanth Goud, Merugu Nikhil, Gantla Sai Kumar Reddy, “AI Medical Diagnosis Application” Proceedings of the Fifth International Conference on Intelligent Computing and Control Systems (ICICCS 2021) IEEE Xplore Part Number: CFP21K74– ART; ISBN: 978-0-7381-1327-2. [Google Scholar]
- T. Mahmud, S. A. Fattah and M. Saquib, “DeepArrNet: An Efficient Deep CNN Architecture for Automatic Arrhythmia Detection and Classification From Denoised ECG Beats,” in IEEE Access, vol. 8, pp. 104788-104800, 2020, doi: 10.1109/ACCESS.2020.2998788. [CrossRef] [Google Scholar]
- Vijayeskar Kumar1, Shahil Kumar1, Krish Kumar Raj1, Mansour H. Assaf1, Voicu Groza2, Rahul R Kumar1, “ECG Multi Class Classification Using Machine Learning Techniques” in 2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) | 978-1-6654-9384– 0/23/$31.00 ©2023 IEEE | DOI: 10.1109/MeMeA57477.2023.10171887. [Google Scholar]
- Borui Hou, Jianyong Yang, Pu Wang, and Ruqiang Yan, Senior Member, IEEE, “LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classication” in IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 69, NO. 4, APRIL 2020. [Google Scholar]
- SIMARJEET KAUR 1, JIMMY SINGLA 1, LEWIS NKENYEREYE 2, SUDAN JHA 3, (Senior Member, IEEE), DEEPAK PRASHAR1, (Member, IEEE), GYANENDRA PRASAD JOSHI4, SHAKER EL-SAPPAGH5,6, MD. SAIFUL ISLAM 7, AND S. M. RIAZUL ISLAM 4, “Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives”, in IEEE ACCESS.2020.3042273, date of publication December 3, 2020. [Google Scholar]
- Amin Ullah, Syed Muhammad Anwar, Muhammad Bilal and Raja Majid Mehmood, “Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation” Remote Sens. 2020, 12, 1685; doi:10.3390/rs12101685. [CrossRef] [Google Scholar]
- M. U. Zahid et al., “Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network,” in IEEE Transactions on Biomedical Engineering, vol.69, no.1, pp.119-128, Jan. 2022, doi:10.1109/TBME.2021 [CrossRef] [Google Scholar]
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