Enhanced Cardiovascular Monitoring Using Radial Basis Function and Deep Belief Network Fusion

Srinivas Naveen Dolu Surabhi ^1*, R.Saraswathi ², S. Sugantha Priya ³, M. Indirani ^4, Neeraj Kumar ⁵, S. Senthil Kumar ⁶

Combining RBF and DBN improves real-time cardiovascular monitoring by enhancing feature extraction and classification accuracy, addressing limitations of conventional methods.

Abstract

Background: Heart disease and other cardiovascular conditions remain a significant global health challenge, contributing to a high number of fatalities annually. Despite technological advancements, current cardiovascular monitoring methods, especially those analyzing electrocardiograms (ECGs), often fail to capture intricate patterns and subtle irregularities, highlighting the need for a more sophisticated approach. This study proposes a novel framework utilizing deep learning, specifically combining the radial basis function (RBF) for feature extraction and a deep belief network (DBN) for classification, to enhance ECG data analysis. Methods: The proposed method involves preprocessing ECG signals to reduce noise, correct baseline drift, and scale amplitude. Feature extraction is performed using the RBF, which captures intricate temporal patterns in the ECG signals. Subsequently, the DBN classifies the extracted features, leveraging its hierarchical learning capabilities to identify subtle correlations and patterns. The model's performance was evaluated using Python simulations on a high-performance computing system, benchmarked against existing methods including Dual-Stage DL, DWT-ML, and M1M2. Results: The RBF-DBN model demonstrated superior performance across several metrics, achieving 99% accuracy, precision, and recall at the 1000th time step, outperforming Dual-Stage DL, DWT-ML, and M1M2 methods. The RBF-DBN method demonstrated a remarkable accuracy of 99%, surpassing the current Dual-Stage DL method by 2%. Additionally, the approach maintained exceptional sensitivity and recall at 99%. The precision of the model was also 99%, and the F1 score, which balances recall and precision, further underscored the model's efficacy in real-time cardiovascular monitoring. Conclusion: The integration of RBF and DBN in ECG analysis significantly enhances the precision and accuracy of cardiovascular monitoring.

Keywords: Deep learning, Cardiovascular monitoring, Electrocardiogram (ECG), Time-series analysis, Real-time intervention, Radial Basis Function (RBF), Deep Belief Network (DBN), Deep Learning

References

Akter, S., Prodhan, R. A., Pias, T. S., Eisenberg, D., & Fresneda Fernandez, J. (2022). M1M2: Deep-learning-based real-time emotion recognition from neural activity. Sensors, 22(21), 8467.

Cho, J. H., Jeong, J. H., & Lee, S. W. (2021). Neurograsp: Real-time EEG classification of high-level motor imagery tasks using a dual-stage deep learning framework. IEEE Transactions on Cybernetics, 52(12), 13279-13292.

Denysyuk, H. V., Pinto, R. J., Silva, P. M., Duarte, R. P., Marinho, F. A., Pimenta, L., ... & Pires, I. M. (2023). Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review. Heliyon.

Hua, X., Han, J., Zhao, C., Tang, H., He, Z., Chen, Q., ... & Zhou, W. (2020). A novel method for ECG signal classification via one-dimensional convolutional neural network. Multimedia Systems, 1-13.

Klosowski, G., Rymarczyk, T., Wójcik, D., Skowron, S., Cieplak, T., & Adamkiewicz, P. (2020). The use of time-frequency moments as inputs of LSTM network for ECG signal classification. Electronics, 9(9), 1452.

Krittanawong, C., Rogers, A. J., Johnson, K. W., Wang, Z., Turakhia, M. P., Halperin, J. L., & Narayan, S. M. (2021). Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management. Nature Reviews Cardiology, 18(2), 75-91.

Murat, F., Yildirim, O., Talo, M., Baloglu, U. B., Demir, Y., & Acharya, U. R. (2020). Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Computers in Biology and Medicine, 120, 103726.

Pandey, S. K., Janghel, R. R., & Vani, V. (2020). Patient specific machine learning models for ECG signal classification. Procedia Computer Science, 167, 2181-2190.

Sahoo, S., Dash, M., Behera, S., & Sabut, S. (2020). Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey. IRBM, 41(4), 185-194.

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.

Shen, M., Wen, P., Song, B., & Li, Y. (2022). An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods. Biomedical Signal Processing and Control, 77, 103820.

Singh, A. K., & Krishnan, S. (2023). ECG signal feature extraction trends in methods and applications. BioMedical Engineering OnLine, 22(1), 1-36.

Smigiel, S., Palczynski, K., & Ledzinski, D. (2021). ECG signal classification using deep learning techniques based on the PTB-XL dataset. Entropy, 23(9), 1121.

Sun, X., Yin, Y., Yang, Q., & Huo, T. (2023). Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. European Journal of Medical Research, 28(1), 242.

Wasimuddin, M., Elleithy, K., Abuzneid, A. S., Faezipour, M., & Abuzaghleh, O. (2020). Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey. IEEE Access, 8, 177782-177803.

Wu, M., Lu, Y., Yang, W., & Wong, S. Y. (2021). A study on arrhythmia via ECG signal classification using the convolutional neural network. Frontiers in Computational Neuroscience, 14, 564015.