Next-Gen ECG Technology: Dual Lead System Empowered with IoT Connectivity for Comprehensive Cardiac Monitoring
DOI:
https://doi.org/10.47981/j.mijst.14(01)2026.554(109-116)Keywords:
Electrocardiogram (ECG), Cardiac Monitoring, Healthcare Technology, Internet of Things (IoT)Abstract
The need for Internet of Things (IoT)- oriented, cost-effective Electrocardiogram (ECG) devices that can remotely measure and transmit ECG signals is becoming increasingly apparent. While standard 12-lead ECG monitoring is effective, its bulky and time-consuming nature poses challenges for remote and continuous monitoring. For developing a cost-effective ECG system, Einthoven's six-lead ECGs are effective but need assessment in real-world conditions. Addressing this gap, this study explores performance evaluation of the dual-lead-to-6-lead ECG monitoring method under 50 Hz noise, electrode reuse, and proximal vs. distal placement. Our system incorporates an ECG circuit that records lead I and lead II data and calculates the 6 leads mathematically. A digital notch filter is applied to the data to enhance signal clarity. The study also includes an arrhythmia-detection component that uses the Pan-Tompkins algorithm to identify abnormal cardiac rhythms promptly. A comprehensive performance analysis of 11 healthy patients was conducted. The reference ECG signals were simultaneously recorded using a BIOPAC MP36 data-acquisition system, with Pearson correlation coefficients ranging from 0.81 to 0.89, with the highest in Lead II. Bland-Altman analysis for R-R interval and Heart rate showed average biases of 11.66 and 0.35, respectively. All data points for both R-R interval and heart rate fell within the ±10% acceptability limits. The proposed technology has potential for telemedicine services and remote patient management, offering an effective solution for continuous, remote ECG monitoring.
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Antoni, L., Bruoth, E., Bugata, P., Gajdos, D., Horvat, S., Hudak, D., Kmecova, V., Stana, R., Stankova, M., Szabari, A., & Vozarikova, G. (2021). A Two-Phase Multilabel ECG Classification Using One-Dimensional Convolutional Neural Network and Modified Labels. Computing in Cardiology. https://doi.org/10.23919/CINC53138.2021.9662878
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Edenbrandt, L., & Pahlm, O. (1988). Vectorcardiogram synthesized from a 12-lead ECG: Superiority of the inverse Dower matrix. Journal of Electrocardiology, 21(4), 361–367. https://doi.org/10.1016/0022-0736(88)90113-6
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Miletic, M. N., Atanasoski, V. A., Belicev, P. P., Gligoric, G. M., Ralevic, U. M., Krsic, J. B., Obradovic, A. D., Lazovic, A., Stojanovic, D. B., Petrovic, J., Babic, R., Vukajlovic, D., Hadzievski, L. R., Bojovic, B. P., Panescu, D., & Vajdic, B. (2024). Accurate Reconstruction of the 12-Lead Electrocardiogram From a 3-Lead Electrocardiogram Measured by a Mobile Device. IEEE Access, 12, 79765–79775. https://doi.org/10.1109/ACCESS.2024.3408412
Nejedly, P., Ivora, A., Smisek, R., Viscor, I., Koscova, Z., Jurak, P., & Plesinger, F. (2021). Classification of ECG Using Ensemble of Residual CNNs with Attention Mechanism. Computing in Cardiology. https://doi.org/10.23919/CINC53138.2021.9662723
Nelwan, S. P., Kors, J. A., Meij, S. H., Van Bemmel, J. H., & Simoons, M. L. (2004). Reconstruction of the 12-lead electrocardiogram from reduced lead sets. Journal of Electrocardiology, 37(1), 11–18. https://doi.org/10.1016/J.JELECTROCARD.2003.10.004
Nguyen, H. H., & Vo, T.T. (2024). Enhancing Cardiovascular Health Monitoring Through IoT and Deep Learning Technologies. SN Computer Science, 5:5, 5(5), 608-. https://doi.org/10.1007/S42979-024-02962-7
Seo, H. C., Yoon, G. W., Joo, S., & Nam, G. B. (2022). Multiple electrocardiogram generator with single-lead electrocardiogram. Computer Methods and Programs in Biomedicine, 221, 106858. https://doi.org/10.1016/J.CMPB.2022.106858
Srivastava, A., Hari, A., Pratiher, S., Alam, S., Ghosh, N., Banerjee, N., & Patra, A. (2021). Channel Self-Attention Deep Learning Framework for Multi-Cardiac Abnormality Diagnosis from Varied-Lead ECG Signals. Computing in Cardiology. https://doi.org/10.23919/CINC53138.2021.9662886
Wang, L. H., Zou, Y. Y., Xie, C. X., Yang, T., & Abu, P. A. R. (2024). Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three‑lead signals. Journal of Electrocardiology, 84, 27–31. https://doi.org/10.1016/J.JELECTROCARD.2024.03.004
Yoon, G. W., & Joo, S. (2024). Classification feasibility test on multi-lead electrocardiography signals generated from single-lead electrocardiography signals. Scientific Reports 2024 14:1, 14(1), 1888-. https://doi.org/10.1038/s41598-024-52216-y
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Copyright (c) 2026 Samir Fazal Manam, Irfan Ahmed Rifat, Samia Jahan, Md. Tobibul Islam, Md Ushama Shafoyat, Wasil Billah
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