Journal of Angiotherapy
Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
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RESEARCH ARTICLE   (Open Access)
Contents Vol 8 (6)

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

 

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Journal of Angiotherapy 8 (6) 1-10 https://doi.org/10.25163/angiotherapy.859536

Submitted: 14 April 2024 Revised: 04 June 2024  Published: 11 June 2024 

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

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