In　addressing　the　key　issues　of　the　data　imbalance　within　ECG　signals　and　modeling　optimization,　we　employed　the　TimeGAN　network　and　a　local　attention　mechanism　based　on　the　artificial　bee　colony　optimization　algorithm　to　enhance　the　performance　and　accuracy　of　ECG　modeling.　Initially,　the　TimeGAN　network　was　introduced　to　rectify　data　imbalance　and　create　a　balanced　dataset.　Furthermore,　the　artificial　bee　colony　algorithm　autonomously　searched　hyperparameter　configurations　by　minimizing　Wasserstein　distance.　Control　experiments　revealed　that　data　augmentation　significantly　boosted　classification　accuracy　to　99.51%,　effectively　addressing　challenges　with　unbalanced　datasets.　Moreover,　to　overcome　bottlenecks　in　the　existing　network,　the　introduction　of　the　Efficient　network　was　adopted　to　enhance　the　performance　of　modeling　optimized　with　attention　mechanisms.　Experimental　results　demonstrated　that　this　integrated　approach　achieved　an　impressive　overall　accuracy　of　99.70%　and　an　average　positive　prediction　rate　of　99.44%,　successfully　addressing　challenges　in　ECG　signal　identification,　classification,　and　diagnosis.　©　2024　the　Author(s).