The　First　China　ECG　Intelligent　Competition　launched　ECG　challenge　to　classify　8　kinds　of　abnormalities　from　uneven　12-lead　ECGs.　These　abnormalities　can　be　classified　into　two　categories　according　to　morphology　and　rhythm,　four　in　each　group.　In　this　paper,　for　morphology　tasks　neural　network　is　applied　mainly　with　input　median　wave　extracted　from　raw　data,　while　traditional　methods　are　executed　and　promoted　by　machine　learning　to　achieve　rhythm　classification.　Non-coexistence　relationship　is　taken　into　consideration　to　fit　in　clinical　significance　better.　The　final　average　F1　score　is　0.886　on　test　set,　which　certificates　these　are　effective　methods　for　ECG　auto　detection.　©　2019,　Springer　Nature　Switzerland　AG.