基于Transformer深度学习模型的心电信号早期诊断先天性心脏病OA

Congenital heart disease(CHD)is one of the most common birth defects worldwide and a major cause of pediatric morbidity and mortality.Early detection is essential for improving survival and long-term outcomes,yet timely diagnosis remains challenging,especially in primary-care and resource-constrained settings.Although electrocardiography(ECG)is inexpensive and widely available,subtle pediatric CHD abnormalities are difficult to detect through manual interpretation,which carries subjectivity and risk of misdiagnosis.Moreover,many existing deep-learning models rely on single-domain convolutional neural network(CNN)or recurrent neural network(RNN)architectures that insufficiently capture long-range temporal dependencies and frequency-domain features.To address these limitations,we propose pediatric AI for cardiac ECG recognition(PACER),a hybrid CNN-Transformer-discrete wavelet transform(DWT)-TabNet framework for automated CHD detection from standard 12-lead pediatric ECG signals.PACER integrates convolutional layers for local morphological extraction,Transformer-based self-attention for long-range temporal modeling,DWT for frequency representation,and TabNet for interpretable multimodal feature fusion.A tailored preprocessing and augmentation pipeline,including SMOTE and Gaussian noise enhancement,improves robustness to class imbalance.Evaluated on 10 344 pediatric ECG recordings using stratified cross-validation,PACER achieved an accuracy of 90.93%,an F1 score of 0.91,and an area under the receiver operating characteristic curve(ROC-AUC)of 0.95,outperforming CNN,RNN,hybrid,Transformer,and CHDdECG baselines.Ablation experiments and model interpretability analysis validate the effectiveness of each module and indicate the potential clinical utility of the proposed method.