BackgroundElectrocardiograms　(ECG)　are　an　important　source　of　information　on　human　heart　health　and　are　widely　used　to　detect　different　types　of　arrhythmias.ObjectiveWith　the　advancement　of　deep　learning,　end-to-end　ECG　classification　models　based　on　neural　networks　have　been　developed.　However,　deeper　network　layers　lead　to　gradient　vanishing.　Moreover,　different　channels　and　periods　of　an　ECG　signal　hold　varying　significance　for　identifying　different　types　of　ECG　abnormalities.MethodsTo　solve　these　two　problems,　an　ECG　classification　method　based　on　a　residual　attention　neural　network　is　proposed　in　this　paper.　The　residual　network　(ResNet)　is　used　to　solve　the　gradient　vanishing　problem.　Moreover,　it　has　fewer　model　parameters,　and　its　structure　is　simpler.　An　attention　mechanism　is　added　to　focus　on　key　information,　integrate　channel　features,　and　improve　voting　methods　to　alleviate　the　problem　of　data　imbalance.ResultsExperiments　and　verifications　are　conducted　using　the　PhysioNet/CinC　Challenge　2017　dataset.　The　average　F1　value　is　0.817,　which　is　0.064　higher　than　that　for　the　ResNet　model.　Compared　with　the　mainstream　methods,　the　performance　is　excellent.