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:

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  A double-threshold adaptive method is used to segment heart sound into each cardiac cycle.

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  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.

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  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.

Graphical abstract

The overall diagram