Provided is a method for constructing an intracardiac abnormal activation point location model based on CNN and LSTM; said model can obtain a patient's 12-lead body surface potential data, while also locating very well the specific location of the abnormal activation point of VT, and obtain the three-dimensional coordinates of the location. The method introduces the idea of deep learning into the location of abnormal activation points of ventricular tachycardia; in the training phase, collected QRS data is used as input; the QRS data corresponds to the three-dimensional coordinates of a mapping point as a label to train a CNN-LSTM network, Conv1D is used to extract features from the input data, LSTM is used for feature fusion in the time domain, and a fully connected layer is used for performing regression prediction of three-dimensional coordinates to finally construct a CNN-LSTM network; the model achieves the prediction of the location of abnormal activation points of VT from a data-driven perspective, effectively solving the time-consuming and laborious problem of clinical catheter ablation.
METHOD FOR CONSTRUCTING INTRACARDIAC ABNORMAL ACTIVATION POINT LOCATION MODEL BASED ON CNN AND LSTM