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Original Article
Edge Computing Enabled Hardware Architecture for Intelligent Cardiac Risk Detection
Reshma N1
M.Sasikala2
G.Kavinaya3
J.Vaishavidevi4
M Shalini5
1 Assistant Professor, Department of Information Technology, Rathinam Technical Campus, Eachanari, Coimbatore, India. 2 3 4 5 Department of Information Technology, Rathinam Technical Campus, Eachanari, Coimbatore, India.
Published Online: March-April 2026
Pages: 179-184
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602026
References
1. S. Rajkomar, E. Oren, K. Chen, A. M. Dai, N. Hajaj, P. Hardt, et al., “Scalable and accurate deep learning for electronic health records,” npj Digital Medicine, vol. 1, no. 18, pp. 1–10, 2018.
A. Hannun, P. Rajpurkar, M. Haghpanahi, G. Tison, C. Bourn, M. Turakhia, and A. Y. Ng, “Cardiologist-level arrhythmia detection with convolutional neural networks,” Nature Medicine, vol. 25, no. 1, pp. 65–69, 2019.
2. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning for healthcare applications,” Nature Biomedical Engineering, vol. 3, no. 6, pp. 463–474, 2019.
3. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 6376–6394, 2019
4. X. Zhang, L. Zhang, and Y. Liu, “Real-time ECG signal classification using lightweight CNN on embedded systems,” IEEE Access, vol. 8, pp. 189 789–189 798, 2020.
5. M. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang, “Disease prediction by machine learning over big healthcare data,” IEEE Access, vol. 7, pp. 111 882–111 892, 2019.
6. H. Elgendi, A. Mohamed, and R. Ward, “Efficient ECG compression and QRS detection for low-power wearable devices,” Biomedical Signal Processing and Control, vol. 52, pp. 227–235, 2019.
7. J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 3, pp. 230–236, 2020.
8. S. Li, Y. Xu, and X. Wang, “Energy-efficient FPGA-based deep learning accelerator for healthcare IoT devices,” IEEE Transactions on Circuits and Systems I, vol. 68, no. 9, pp. 3560–3572, 2021.
9. T. Nguyen, A. K. Sangaiah, G. Srivastava, and P. Zhang, “Edge AI for smart healthcare monitoring systems: A survey,” Future Generation Computer Systems, vol. 117, pp. 341–357, 2021.
A. Hannun, P. Rajpurkar, M. Haghpanahi, G. Tison, C. Bourn, M. Turakhia, and A. Y. Ng, “Cardiologist-level arrhythmia detection with convolutional neural networks,” Nature Medicine, vol. 25, no. 1, pp. 65–69, 2019.
2. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning for healthcare applications,” Nature Biomedical Engineering, vol. 3, no. 6, pp. 463–474, 2019.
3. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 6376–6394, 2019
4. X. Zhang, L. Zhang, and Y. Liu, “Real-time ECG signal classification using lightweight CNN on embedded systems,” IEEE Access, vol. 8, pp. 189 789–189 798, 2020.
5. M. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang, “Disease prediction by machine learning over big healthcare data,” IEEE Access, vol. 7, pp. 111 882–111 892, 2019.
6. H. Elgendi, A. Mohamed, and R. Ward, “Efficient ECG compression and QRS detection for low-power wearable devices,” Biomedical Signal Processing and Control, vol. 52, pp. 227–235, 2019.
7. J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 3, pp. 230–236, 2020.
8. S. Li, Y. Xu, and X. Wang, “Energy-efficient FPGA-based deep learning accelerator for healthcare IoT devices,” IEEE Transactions on Circuits and Systems I, vol. 68, no. 9, pp. 3560–3572, 2021.
9. T. Nguyen, A. K. Sangaiah, G. Srivastava, and P. Zhang, “Edge AI for smart healthcare monitoring systems: A survey,” Future Generation Computer Systems, vol. 117, pp. 341–357, 2021.
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