Automatic　heartbeat　classification　technology　based　on　the　ECG　plays　an　important　role　in　assisting　doctors　with　arrhythmia　diagnosis.　While　many　heartbeat　classification　studies　can　achieve　good　performance　under　the　intra-patient　paradigm,　they　still　cannot　offer　acceptable　classification　results　under　the　inter-patient　paradigm.　Additionally,　the　available　ECG　datasets　are　highly　class　imbalanced　since　normal　heartbeats　appear　much　more　frequently　than　abnormal　heartbeats,　resulting　in　most　methods　having　low　sensitives　and　positive　predictive　values　on　minority　class　ectopic　heartbeats.　To　solve　the　above　problems,　this　study　proposes　an　automatic　ECG　heartbeat　classification　method　based　on　ensemble　learning　and　multi-kernel　learning.　First,　we　use　a　linear　combination　of　the　radial　basis　function　kernel　and　the　polynomial　kernel　to　produce　a　mixed-kernel-based　extreme　learning　machine　(MKELM).　Then,　a　MKELM-based　random　forest　binary　classifier　(MKELM-RF)　is　constructed.　Finally,　an　ensemble　multiclass　classifier　MKELM-RF-OVO　is　proposed　based　on　one-vs.-one　(OVO)　reduction　and　MKELM-RF.　We　evaluated　the　proposed　method　on　the　public　MIT-BIH-AR　benchmarks　database,　under　the　inter-patient　paradigm,　classifying　four　types　of　heartbeats,　namely,　normal　beats　(N),　supraventricular　ectopic　beats　(S),　ventricular　ectopic　beats　(V)　and　the　fusion　of　ventricular　and　normal　(F).　The　obtained　overall　accuracy　and　the　average　positive　predictive　value　are　98.1%　and　93.9%,　respectively,　which　are　higher　than　the　current　studies　by　approximately　4%　and　6%,　respectively.　The　sensitivities　for　classes　S　and　V　are　1　and　94.4%,　respectively,　which　outperforms　most　methods.　The　evaluation　results　show　that　our　proposed　method　achieves　a　superior　classification　performance　compared　to　the　state-of-the-art　methods.　©　2020