When　doctors　judge　myocardial　infarction　(MI),　they　often　introduce　12　leads　as　the　basis　for　judgment.　However,　the　repetitive　labeling　of　nonlinear　ECG　signals　is　time-consuming　and　laborious.　There　is　a　need　of　computer-aided　techniques　for　automatic　ECG　signal　analysis.　In　this　paper,　we　proposed　a　new　method　based　on　median　complexes　and　convolutional　neural　networks　(CNNs)　for　MI　detection　and　location.　Median　complexes　were　extracted　which　retained　the　morphological　features　of　MIs.　Then,　the　CNN　was　used　to　determine　whether　each　lead　presented　MI　characteristics.　Finally,　the　information　of　the　12　leads　was　synthesized　to　realize　the　location　of　MIs.　Six　types　of　MI　recognition　were　performed,　including　inferior,　lateral,　anterolateral,　anterior,　and　anteroseptal　MIs,　and　non-MI.　We　investigated　cross-database　performance　for　MI　detection　and　location　by　the　proposed　method,　with　the　CNN　models　trained　on　a　local　database　and　validated　by　the　open　PTB　database.　Experimental　results　showed　that　the　proposed　method　yielded　F1　scores　of　84.6%　and　80.4%　for　the　local　and　PTB　test　datasets,　respectively.　The　proposed　method　outperformed　the　traditional　hand-crafted　method.　With　satisfying　cross-database　and　generalization　performance,　the　proposed　CNN　method　may　be　used　as　a　new　method　for　improved　MI　detection　and　location　in　ECG　signals.　©　2022,　The　Author(s).