Key result
Particle swarm optimization-enhanced deep neural networks achieve ~99.8% accuracy for ECG arrhythmia classification.
A PSO-enhanced deep neural network achieves high accuracy (99.76%) in automated ECG arrhythmia classification, reducing manual hyperparameter tuning effort.
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PSO-optimized DNNs merit external validation for ECG classification; leaves open clinical adoption pending prospective trials.
This study proposes an ECG classification system using particle swarm optimization (PSO) for automated deep neural network hyperparameter tuning. PSO optimizes five key parameters: neuron counts in two fully connected layers, dropout rate, learning rate, and optimizer selection. ECG signals undergo wavelet decomposition for feature extraction, with classification performed on the MIT-BIH Arrhythmia Database across five heartbeat classes. The PSO-optimized model achieves superior performance with 99.76% accuracy, 99.34% precision, and 99.21% F1 score, demonstrating PSO's effectiveness in improving model reliability while reducing manual effort.
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Kahlessenane et al. (2025) studied ECG arrhythmias. Particle swarm optimization-enhanced deep neural network was evaluated on Classification accuracy. A particle swarm optimization-enhanced deep neural network for ECG arrhythmia classification achieved 99.76% accuracy, 99.34% precision, and 99.21% F1 score on the MIT-BIH database.
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