The　accurate　classification　of　electrocardiogram　(ECG)　signals　is　crucial　for　the　early　detection　and　diagnosis　of　cardiovascular　diseases　(CVDs),　which　remain　the　leading　cause　of　mortality　globally.　Traditional　methods　of　ECG　interpretation　are　often　limited　by　their　inability　to　simultaneously　identify　multiple　co-occurring　heart　conditions,　a　challenge　that　is　further　complicated　by　the　complex　nature　of　ECG　signals.　In　response　to　this,　we　introduce　an　innovative　deep　learning　architecture　that　combines　Efficient　Channel　Attention　(ECA),　Squeeze-and-Excitation　Networks　(SENet),　and　residual　networks　(Resnet)　modules,　specifically　designed　for　multi-label　ECG　classification　using　the　PTB-XL　dataset.　Our　model　not　only　excels　in　detecting　and　distinguishing　between　various　cardiac　abnormalities　but　also　addresses　the　critical　need　for　interpretability　in　clinical　applications.　By　incorporating　SHapley　Additive　exPlanations　(SHAP)　and　Gradient-weighted　Class　Activation　Mapping　(Grad-CAM),　we　provide　transparent,　explainable　insights　into　the　model＇s　decision-making　process,　ensuring　that　healthcare　professionals　can　understand　and　trust　the　model＇s　predictions.　Experimental　results　on　the　PTB-XL　dataset　demonstrate　that　the　proposed　model　achieves　Exact　match,　Accuracy　and　F1-score　of　0.638,　88.27%　and　91.81%,　respectively,　with　substantial　improvements　in　both　sensitivity　and　specificity　across　various　cardiac　conditions　compared　to　state-of-the-art　methods.　This　work　represents　a　crucial　step　toward　more　reliable,　interpretable,　and　clinically　applicable　AI-driven　diagnostics　for　cardiovascular　health.　©　2025　Elsevier　Ltd