Key result
Particle swarm-optimized CNN achieves ~100% accuracy for R and T peak detection in ECG signals.
Why the study?
R and T peaks in ECG signals suffer from noise and variability, requiring automated, optimized machine learning approaches to improve accuracy and reduce manual analysis.
Does a CNN optimized with Particle Swarm Optimization improve the detection of R and T peaks in ECG signals?
Does a CNN optimized with Particle Swarm Optimization improve the detection of R and T peaks in ECG signals?
An optimized CNN using Particle Swarm Optimization achieved >99% accuracy for detecting R and T peaks in ECG signals, offering a robust tool for real-time cardiac monitoring.
Enhances automated ECG monitoring accuracy for clinicians; extends CNN validation in cardiovascular signal processing.
Detection of R and T peaks in electrocardiogram (ECG) signals is essential for monitoring and diagnosing heart-related diseases. In generic cases, these peaks suffer through different types of noise and variability, which are of important clinical significance in identifying diseases like myocardial infarction, ischemia, and electrolyte imbalance. Machine learning techniques are integrated computer-aided diagnosis systems (CIDS) [1] to reduce manual effort in peak analysis by medical professionals. Machine learning models are used for learning from ECG nonlinear patterns and tested to mark actual R and T peaks. Once the machine learning model is trained and tested. Minimizing error or loss in the implemented machine learning model is possible through tuning parameters and finding the best fitness value. This article proposed an optimized metaheuristic approach (finding best fitness) for convolutional neural network (CNN), to detect R and T peaks in captured ECG, aimed, at improving the accuracy and robustness of real-time signal data. Before training and testing optimization, different approaches of metaheuristics optimization were evaluated based on best fitness values over iteration and found optimization technique having lowest convergence. Applying Particle Swarm Optimization (PSO) with CNN achieved 99.57% accuracy, 99.13% precision, 99.28% recall, and 99.25% F1-Score on the publicly available ECG database. The paper results that the proposed approach not only detects peaks but also improves the capabilities of the model for real-time cardiac monitoring systems development.
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Maurya et al. (2024) studied ECG signal analysis. Particle Swarm Optimization (PSO) with Convolutional Neural Network (CNN) vs. Other metaheuristics optimization approaches was evaluated on R and T peak detection accuracy. Applying Particle Swarm Optimization with a convolutional neural network achieved 99.57% accuracy, 99.13% precision, 99.28% recall, and 99.25% F1-Score for R and T peak detection in ECG signals.
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