Portable ECG Prototype based on Arduino and Random Forest Classification for Home Heart-Rate Monitoring
Submitted : 2025-07-05, Published : 2025-08-19.
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A. Abdelrazik et al., “Wearable Devices for Arrhythmia Detection: Advancements and Clinical Implications,” Sensors, vol. 25, no. 9, pp. 1–28, 2025. https://doi.org/10.3390/s25092848
J. Z. Metcalfe et al., “Validation of a Handheld 6-Lead Device for QT Interval Monitoring in Resource-Limited Settings,” JAMA Netw. Open, vol. 7, no. 6, p. E2415576, 2024. https://doi.org/10.1001/jamanetworkopen.2024.15576
J. Medina-Avelino, R. Silva-Bustillos, and J. A. Holgado-Terriza, “Are Wearable ECG Devices Ready for Hospital at Home Application?,” Sensors, vol. 25, no. 10, 2025. https://doi.org/10.3390/s25102982
T. Moller, Y. Georgie, M. Voss, and L. Kaltwasser, “An Arduino Based Heartbeat Detection Device (ArdMob-ECG) for Real-Time ECG Analysis,” 2022 IEEE Signal Process. Med. Biol. Symp. SPMB 2022 - Proc., pp. 1–26, 2022. https://doi.org/10.1109/SPMB55497.2022.10014819
A. A. Yusuf, N.-N. Nnenna Harmony, D. P. Eze-Steven, and N. Charles N, “Design and Implementation of Portable Low-Cost Heart Rate Monitoring ECG System,” Eng. Technol. J., vol. 10, no. 01, 2025. https://doi.org/10.47191/etj/v10i01.05
J. Heaney, J. Buick, M. U. Hadi, and N. Soin, “Internet of Things-Based ECG and Vitals Healthcare Monitoring System,” Micromachines, vol. 13, no. 12, 2022. https://doi.org/10.3390/mi13122153
S. Smigiel, K. Pałczyński, and D. Ledziński, “ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset,” Entropy, vol. 23, no. 9, p. 1121, 2021. https://doi.org/10.3390/e23091121
E. Kim, J. Kim, J. Park, H. Ko, and Y. Kyung, “TinyML-Based Classification in an ECG Monitoring Embedded System,” Comput. Mater. Contin., vol. 75, no. 1, pp. 1751–1764, 2023. https://doi.org/10.32604/cmc.2023.031663
T. Subba and T. Chingtham, “Comparative Analysis of Machine Learning Algorithms With Advanced Feature Extraction for ECG Signal Classification,” IEEE Access, vol. 12, no. March, pp. 57727–57740, 2024. https://doi.org/10.1109/ACCESS.2024.3387041
C. Prajitha, K. P. Sridhar, and S. Baskar, “ECG diagnosis for arrhythmia detection with a cloud-based service and a wearable sensor network in a smart city environment,” Front. Sustain. Cities, vol. 4, 2022. https://doi.org/10.3389/frsc.2022.1073486
Y. Niu, H. Wang, H. Wang, H. Zhang, Z. Jin, and Y. Guo, “Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection,” Digit. Heal., vol. 9, 2023. https://doi.org/10.1177/20552076231198682
M. Bravo-Zanoguera, D. Cuevas-González, J. P. García-Vázquez, R. L. Avitia, and M. A. Reyna, “Portable ECG System Design Using the AD8232 Microchip and Open-Source Platform,” in Proc. 6th Int. Electron. Conf. Sensors Appl., 2020, p. 49, 2020. https://doi.org/10.3390/ecsa-6-06584
R. E. Cañón-Clavijo, C. E. Montenegro-Marin, P. A. Gaona-Garcia, and J. Ortiz-Guzmán, “IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning,” J. Healthc. Eng., vol. 2023. https://doi.org/10.1155/2023/6401673
K. Rjoob et al., “Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications,” Artif. Intell. Med., vol. 132, p. 102381, Oct. 2022. https://doi.org/10.1016/j.artmed.2022.102381
M. D. Nadeem, M. T. I. Ansari, P. Shekhar Pandey, A. Shadab, S. Kumar Raghuwanshi, and S. Kumar, “Recent advances of ECG monitoring and webserver health monitoring applications: A review,” Opt. Laser Technol., vol. 177, p. 111039, Oct. 2024. https://doi.org/10.1016/j.optlastec.2024.111039
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