EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 698 (2026) E3S Web Conf., 698 (2026) 01015 References
Open Access
Issue
E3S Web Conf.
Volume 698, 2026
First International Conference on Research and Advancements in Electronics, Energy, and Environment (ICRAEEE 2025)
Article Number 01015
Number of page(s) 6
Section Electrical and Electronic Engineering
DOI https://doi.org/10.1051/e3sconf/202669801015
Published online 16 March 2026
  1. Busi, K. C., Krishna, T. S. R., Battula, B. P., Jyothi, P., Sathish, K., & Narayana, Y. V. (2025). Edge ai for energy-efficient real-time health monitoring in iot medical devices. https://doi.org/10.4018/979-8-3373-4134-7.ch009 [Google Scholar]
  2. Braojos, R., Mamaghanian, H., Junior, A. D., Ansaloni, G., Atienza, D., Rincon, F., & Murali, S. (2014). Ultra-low power design of wearable cardiac monitoring systems. https://doi.org/10.1145/2593069.2596691 [Google Scholar]
  3. Loroch, D., Feldmann, J., Rybalkin, V., & Wehn, N. (2025). Low-power, energy-efficient, cardiologist-level atrial fibrillation detection for wearable devices. arXiv.org, abs/2508.13181 null, https://doi.org/10.48550/arxiv.2508.13181 [Google Scholar]
  4. Loh, J., Wen, J., & Gemmeke, T. (2020). Low-cost dnn hardware accelerator for wearable, high-quality cardiac arrythmia detection. https://doi.org/10.1109/ASAP49362.2020.00042 [Google Scholar]
  5. Busia, P., Scrugli, M. A., Jung, V. J. B., Benini, L., & Meloni, P. (2024). A noisy beat is worth 16 words: A tiny transformer for low-power arrhythmia classification on microcontrollers. arXiv.org, abs/2402.10748 null https://doi.org/10.48550/arxiv.2402.10748 [Google Scholar]
  6. Mukhopadhyay, S., Dey, S., Ghose, A., & Dasgupta, P. (2023). Generating tiny deep neural networks for ecg classification on micro-controllers. https://doi.org/10.1109/PerComWorkshops56833.2023.10150311 [Google Scholar]
  7. Tsai, I. H., & Morshed, B. I. (2025). Signal-specific and signal-independent features for real time beat-by-beat ecg classification with AI for cardiac abnormality detection. Electronics, 14 (13), 2509-2509. https://doi.org/10.3390/electronics14132509 [Google Scholar]
  8. Dong, B. A., Tran, D., & Vy, N. H. A. (2025). Harnessing tinyml for accurate ecg beat detection.https://doi.org/10.1109/icdv66179.2025.11135036 [Google Scholar]
  9. Xie, Y., Lin, X., & Lin, C. (2022). Sembednet: Hardware-friendly cnn for ectopic beat classificationon stm32-based edge device. https://doi.org/10.1109/RASSE54974.2022.9989708 [Google Scholar]
  10. Żyliński, M., Nassibi, A., & Mandic, D. P. (2023). Design and implementation of an atrial fibrillation detection algorithm on the arm cortex-m4 microcontroller.. https://doi.org/10.3390/s23177521 [Google Scholar]
  11. Misra, M. (2025). Low-power AI model optimization for wearable health monitoring applications. International Journal For Science Technology And Engineering, 13 (9), 55-64. https://doi.org/10.22214/ijraset.2025.73973 [Google Scholar]
  12. Wong, D. T., Li, Y., John, D., Ho, W. K., & Heng, C. (2021). Resource and energy efficient implementation of ecg classifier using binarized cnn for edge AI devices. https://doi.org/10.1109/ISCAS51556.2021.9401427 [Google Scholar]
  13. Akter, M., Islam, M. N., Ahad, A., Chowdhury, M.A. B., Apurba, F. F., & Khan, R. (2024). An embedded system for real-time atrial fibrillation diagnosis using a multimodal approach to ecg data. Eng, 5 (4), 2728-2751. https://doi.org/10.3390/eng5040143 [Google Scholar]
  14. Deng, J., Yang, J., Wang, X., & Zhang, X. (2024). A novel instruction driven 1-d cnn processor for ecg classification. Sensors, 24 (13), 4376-4376. https://doi.org/10.3390/s24134376 [Google Scholar]
  15. Hoyer, I., Utz, A., Ludecke, A., Seidl, K., Roßman, Ö., Straczek, L., Akboyraz, O., & Hessel, S. (2023). The artemis project: Mixed-signal ic for edge-ai-based classification of ecg signals. Current Directions in Biomedical Engineering, 9 null327-330. https://doi.org/10.1515/cdbme-2023-1082 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.