The invention provides a coronary heart disease electrocardiogram screening system based on a residual neural network, and the system comprises an electrocardiosignal processing module which is used for extracting electrocardiosignals generated by an electrocardiograph, carrying out the multi-layer decomposition and noise reduction of the electrocardiosignals through employing a symlets4 wavelet, searching the position of an R wave in electrocardiosignal sequence data converted from the electrocardiosignals, and carrying out the heart beat segmentation based on the position of the R wave; a depth feature extraction module which is used for performing translation and scaling on the data processed by the electrocardiosignal processing module to enhance the data, and extracting the depth features of the twelve-lead electrocardiogram by using a ResneXt50 network comprising an extrusion and excitation network module; and a tree model prediction module which is used for combining the depth features extracted by the depth feature extraction module with electrocardiograph data, and inputting the trained XGBoost model to obtain the prediction probability of the coronary heart disease electrocardio features in the electrocardiogram. The system is low in installation and use cost, can achieve automatic screening, is higher in accuracy compared with diagnosis of an electrocardiograph, can reduce misjudgment or missed judgment, and reduces the workload of doctors.
Coronary heart disease electrocardiogram screening system and method based on residual neural network