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This study introduces an adaptive deep reinforcement learning framework for ECG signal analysis aimed at enhancing arrhythmia prediction. The proposed model automates hyperparameter tuning for a Convolutional Neural Network (CNN), achieving a high accuracy of 97.4% while minimizing resource consumption and complexity. Compared to non-optimized and manually fine-tuned models, the framework demonstrates superior performance in predicting arrhythmias, establishing its potential as an efficient solution in the

Author

ro0tmo.bspeowen

Language

EN