ATC-CNN-Based IoT Framework for Real-Time Cardiovascular Monitoring: A Comparative Analysis with Deep Learning Models

Dev Ras Pandey ¹*, Ahilya Dubey ¹

This study determines the ATC-CNN’s superior performance in real-time monitoring of cardiovascular patients revolutionizes care, enhancing access, accuracy, and clinical outcomes.

Abstract

Background: Cardiovascular diseases (CVDs), including hypertension, coronary heart disease, and ventricular fibrillation, remain the leading cause of death worldwide. The COVID-19 pandemic accelerated the adoption of telecardiology to minimize in-person interactions, enhancing access to cardiovascular care, especially in remote areas. However, traditional real-time monitoring (RTM) systems often require patient visits, creating a need for cost-effective, user-friendly alternatives that leverage IoT and AI for improved patient management. Methods: This study developed an IoT-enabled RTM system integrated with an Adaptive Thresholding Classifier-Convolutional Neural Network (ATC-CNN) framework for managing cardiovascular patients (CP). Data from a heart failure dataset in the UCI Machine Learning Archive was used to train and evaluate the model. Multiple deep learning (DL) algorithms, including MLP, CNN, LSTM, RNN, and the proposed ATC-CNN, were assessed based on accuracy, precision, recall, and F1 scores. Results: The ATC-CNN model achieved an accuracy of 0.9831, outperforming other DL models, including MLP, CNN, LSTM, and RNN. It demonstrated superior capabilities in handling complex cardiovascular data, offering reliable and timely diagnosis with high precision and recall. The integration of IoT and AI facilitated continuous monitoring and real-time data analysis, addressing key limitations of traditional RTM systems. Conclusion: The ATC-CNN framework shows great potential for revolutionizing cardiovascular care by enhancing remote monitoring capabilities and clinical decision-making. It provides an effective, real-time solution for early diagnosis and intervention, particularly in remote and underserved areas. Future research should focus on expanding datasets and refining the model for broader adaptability in diverse healthcare settings.

Keywords: Cardiovascular diseases, Real-time monitoring, IoT, AI, ATC-CNN

References

Bhuiyan, M. N., Billah, M. M., Bhuiyan, F., Bhuiyan, M. A. R., Hasan, N., Rahman, M. M., ... & Niu, M. (2022). Design and implementation of a feasible model for the IoT-based ubiquitous healthcare monitoring system for rural and urban areas. IEEE Access, 10, 91984-91997.

Birje, M. N., & Hanji, S. S. (2020). Internet of things-based distributed healthcare systems: a review. Journal of Data, Information and Management, 2(3), 149-165.

Camgözlü, Y., & Kutlu, Y. (2023). Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Natural and Engineering Sciences, 8(3), 214-232.

Dahiya, E. S., Kalra, A. M., Lowe, A., & Anand, G. (2024). Wearable technology for monitoring electrocardiograms (ECGs) in adults: a scoping review. Sensors, 24(4), 1318.

Das, S., Pradhan, S. K., Mishra, S., Pradhan, S., & Pattnaik, P. K. (2022). Diagnosis of cardiac problem using rough set theory and machine learning. Indian Journal of Computer Science and Engineering, 13(4), 1112-1131.

Giovanni, A., Enrico, A., Aime, B., Michael, B., Marianne, B., Jonathan, C., ... & Mayowa, O. (2020). Global Burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. Journal of the American College of Cardiology, 76(25), 2982-3021.

Jothiramalingam, R., Jude, A., Patan, R., Ramachandran, M., Duraisamy, J. H., & Gandomi, A. H. (2021). Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal. Neural Computing and Applications, 33, 4445-4455.

Lavanya, P., Subba, R.I.V., Selvakumar, V. & Shreesh V Deshpande. (2024). An Intelligent Health Surveillance System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa Communication and Internet of Medical Things (IoMT). Journal of Internet Services and Information Security, 14(1), 165-179.

Lih, O. S., Jahmunah, V., San, T. R., Ciaccio, E. J., Yamakawa, T., Tanabe, M., ... & Acharya, U. R. (2020). Comprehensive electrocardiographic diagnosis based on deep learning. Artificial intelligence in medicine, 103, 101789.

Ma, F., Zhang, J., Liang, W., & Xue, J. (2020). Automated classification of atrial fibrillation using artificial neural network for wearable devices. Mathematical Problems in Engineering, 2020(1), 9159158.

Majumder, S., Roy, A. K., Jiang, W., Mondal, T., & Deen, M. J. (2023). Smart Textiles to Enable In-Home Health Care: State of the Art, Challenges, and Future Perspectives. IEEE Journal on Flexible Electronics.

Moghadas, E., Rezazadeh, J., & Farahbakhsh, R. (2020). An IoT patient monitoring based on fog computing and data mining: Cardiac arrhythmia use case. Internet of Things, 11, 100251.

Molinari, G., Molinari, M., Di Biase, M., & Brunetti, N. D. (2018). Telecardiology and its settings of application: An update. Journal of telemedicine and telecare, 24(5), 373-381.

Mumtaj Begum, H. (2022). Scientometric Analysis of the Research Paper Output on Artificial Intelligence: A Study. Indian Journal of Information Sources and Services, 12(1), 52–58.

Pani, D., Dessì, A., Saenz-Cogollo, J. F., Barabino, G., Fraboni, B., & Bonfiglio, A. (2015). Fully textile, PEDOT: PSS based electrodes for wearable ECG monitoring systems. IEEE Transactions on Biomedical Engineering, 63(3), 540-549.

Ribeiro, A. H., Ribeiro, M. H., Paixão, G. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., ... & Ribeiro, A. L. P. (2020). Automatic diagnosis of the 12-lead ECG using a deep neural network. Nature Communications, 11(1), 1760.

Sangaiah, A. K., Arumugam, M., & Bian, G. B. (2020). An intelligent learning approach for improving ECG signal classification and arrhythmia analysis. Artificial Intelligence in Medicine, 103, 101788.

Satish S., & Herald, A.R. (2024). Fuzzy Attention U-Net Architecture Based Localization and YOLOv5 Detection for Fetal Cardiac Ultrasound Images. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 15(1), 01-16.

Seferagic, A., Famaey, J., De Poorter, E., & Hoebeke, J. (2020). Survey on wireless technology trade-offs for the industrial Internet of things. Sensors, 20(2), 488.

Sumiati., Penny, H., Agung, T., Afrasim, Y., & Achmad, S. (2024). Classification of Heart Disorders Using an Artificial Neural Network Approach. Journal of Internet Services and Information Security, 14(2), 189-201.

Surendar, A., Veerappan, S., Sadulla, S., & Arvinth, N. (2024). Lung cancer segmentation and detection using KMP algorithm. Onkologia i Radioterapia, 18(4).

Tanner, F. C., Brooks, N., Fox, K. F., Goncalves, L., Kearney, P., Michalis, L., ... & Kirchhof, P. (2020). ESC core curriculum for the cardiologist. European Heart Journal, 41(38), 3605-3692.

Xu, X., Liu, Z., He, P., & Yang, J. (2019). Screen printed silver nanowire and graphene oxide hybrid transparent electrodes for long-term electrocardiography monitoring. Journal of Physics D: Applied Physics, 52(45), 455401.