Hybrid convolutional neural network-transformer model for automated and accurate electrocardiogram signal analysis

Early identification and classification of cardiac abnormalities entirely depends on Electrocardiogram (ECG) analysis. This research presents an optimized deep learning framework which unites Convolutional Neural Networks (CNN) with Transformer-based Multi-Head Attention modules for performing ECG signal classification. Researchers used the PTB Diagnostic ECG Database to conduct training along with evaluation while performing preprocessing on ECG signals followed by window segmenting and normalization processes. A CNN layer sequence in the model allows extraction of spatial features while a Transformer-based attention mechanism discovers extended signal dependencies. The training used categorical cross-entropy loss with Adam optimizer to reach high classification accuracy. The model effectiveness for ECG signal classification can be assessed by performance evaluation using confusion matrices and ROC curves and precision-recall metrics. The proposed combined CNN-Transformer architecture demonstrates strong potential for automatic disease diagnosis in cardiology which advances clinical support technologies.