Point Calculation Detection System Based on In Suit Convolution Transistor for Clinical Diagnosis of Heart Disease

Abstract 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 ≈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.