Robust 12-Lead ECG Classification with Lightweight ResNet: An Adaptive Second-Order Learning Rate Optimization Approach

To enhance the classification accuracy of the ResNet model for 12-lead ECG signals, a novel approach that focuses on optimizing the learning rate within the model training algorithm is proposed. Firstly, a Taylor expansion of the training formula for model weights is performed to derive a learning rate that incorporates the second-order gradient information. Subsequently, to circumvent the direct computation of the complex second-order gradient in the learning rate, an approximation method utilizing the historical first-order gradient is introduced. Additionally, truncation techniques are employed to ensure that the second-order learning rate remains neither excessively large nor too small. Ultimately, the 1D-ResNet-AdaSOM model is constructed based on this adaptive second-order momentum (AdaSOM) method and applied for 12-lead ECG classification. The proposed algorithm and model were validated on the CPSC2018 dataset. The evolving trend of the loss function throughout the training process demonstrated that the proposed algorithm exhibited commendable convergence and stability, and these results aligned with the conclusions derived from the theoretical analysis of the algorithm’s convergence. On the test set, the model attained an impressive average F1 score of 0.862, demonstrating that 1D-ResNet-AdaSOM surpassed several state-of-the-art deep-learning models in performance while exhibiting strong robustness. The experimental findings further substantiate our hypothesis that adjusting the learning rate in the ResNet training algorithm can effectively enhance classification accuracy for 12-lead ECGs.