Background:　Electrocardiograms　(ECG)　are　an　important　source　of　information　on　human　heart　health　and　arewidely　used　to　detect　different　types　of　arrhythmias.　Objective:　With　the　advancement　of　deep　learning,　end-to-end　ECG　classification　models　based　on　neuralnetworks　have　been　developed.　However,　deeper　network　layers　lead　to　gradient　vanishing.　Moreover,　differentchannels　and　periods　of　an　ECG　signal　hold　varying　significance　for　identifying　different　types　of　ECGabnormalities.　Methods:　To　solve　these　two　problems,　an　ECG　classification　method　based　on　a　residual　attention　neural　networkis　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　onkey　information,　integrate　channel　features,　and　improve　voting　methods　to　alleviate　the　problem　of　dataimbalance.　Results:　Experiments　and　verifications　are　conducted　using　the　PhysioNet/CinC　Challenge　2017　dataset.　Theaverage　F1　value　is　0.817,　which　is　0.064　higher　than　that　for　the　ResNet　model.　Compared　with　the　mainstreammethods,　the　performance　is　excellent.　©　The　Author(s)　under　exclusive　licence　to　Biomedical　Engineering　Society　2024.