Electrocardiograph　signals　reflect　the　current　state　of　the　heart　and　have　great　significance　to　the　clinical　diagnosis　of　the　heart.　Convolutional　neural　networks　perform　excellently　in　electrocardiograph　pattern　recognition.　However,　CNNs　processing　ECG　signals　need　to　convert　them　from　1D　to　2D,　leading　to　additional　circuit　and　time　costs　in　hardware.　Here,　a　convolution　organic　transistor　(COT)　is　proposed　for　monitoring　the　ECG　with　CNNs.　Based　on　the　surface　electric　field　effect　and　trap　effect,　COT　can　directly　process　1D　ECG　data　without　complex　preprocessing.　It　can　complete　the　convolution　calculation　of　ECG　signals　approximate　to　20　000　times　per　second　in　theory　and　reduce　the　number　of　devices　by　83%　compared　to　conventional　arrays.　Further,　actual　ECG　signals　are　measured　and　input　into　the　COT,　which　can　initially　recognize　the　type　of　ECG　abnormality.　Finally,　a　point　calculation　detection　system　is　established　with　96.2%　recognition　accuracy　in　the　five-heartbeat　classification　task　by　combining　the　1D　CNN.　This　work　proposes　an　in　suit　convolutional　organic　transistor,　which　can　directly　process　1D　ECG　data　without　complex　preprocessing,　realizing　the　preliminary　judgment　of　the　type　of　abnormal　ECG　at　the　level　of　a　single　device.　This　strategy　provides　an　effective　scheme　for　the　development　of　real-time　and　portable　ECG　monitoring　equipment.　image