Abstract: Pulmonary arterial hypertension(PAH) associated with congenital heart disease has a high clinical morbidity,disability and mortality.Its diagnosis is mainly made by measuring the mean pulmonary arterial pressure by right heart catheterization.This method is invasive and has high operational requirements,and it is inconvenient to be used in screening,so it is of great significance to explore a non-invasive CHD-PAH intelligent auxiliary diagnosis scheme.This paper studies CHD-PAH on the basis of congenital heart disease,starting from the analysis of ECG signal,and modeling and predicting CHD-PAH by means of preprocessing,heart beat segmentation,waveform detection,feature extraction,data expansion,classification and identification.Based on the Christov_segmenter algorithm,the differential threshold and local peak improvement are used to detect QRS waves,P waves and T waves,and finally extract bimodal features based on time and amplitude.In order to fit the best classification model,the support vector machine,random forest and K-neighbor classifiers are used in experiments,and a sparrow search algorithm based on T distribution is designed to improve the support vector machine.A total of 460 1-lead ECG signals with a duration of 20 s are used for training and testing.Experimental results show that the prediction accuracy,specificity and sensitivity of the SVM model optimized by the proposed algorithm can reach 99.76%,99.80% and 99.73%,respectively.

Key words: Electrocardiograph(ECG), Congenital heart disease(CHD), Pulmonary arterial hypertension(PAH), Classifier, Machine learning