Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension

Abstract

Pulmonary arterial hypertension associated with congenital heart disease (CHD-PAH) is a fatal cardiovascular disease. A novel method for non-invasive initial diagnosis of the CHD-PAH was put forward in this work. First, original heart sounds were segmented into each cardiac cycle by using double-threshold adaptive method. According to clinical auscultation, the pathological information of CHD-PAH is concentrated in S2, so the time-frequency features in both of an entire cardiac cycle and S2 were extracted. Then the time-frequency features combine with the deep learning features to form a feature vector. It is the fusion feature, which will be input into a classifier. Finally, the majority voting algorithm was used to obtain the optimal classification results. A classification accuracy of 88.61% was achieved using this novel method. Three points are essential:•A double-threshold adaptive method is used to segment heart sound into each cardiac cycle.•The time-frequency domain features in both of an entire cardiac cycle and S2 were extracted, which are combined with deep learning features to form the fusion feature.•The XGBoost was used as three-class classifier for the classification of normal, CHD and CHD-PAH. The majority voting algorithm was used to obtain the optimal classification results.