Detection of pulmonary arterial hypertension associated with congenital heart disease based on time–frequency domain and deep learning features

Abstract

The heart sounds reflect the health of the heart. Its recording is the phonocardiogram (PCG). Pulmonary arterial hypertension associated with congenital heart disease (CHD-PAH) is a serious heart disease and is often associated with severe disability and death. The disease is not well characterized onset. The most patients are severe when they have been diagnosed and miss the best time to treat them. The objective of this study was to develop a computer aided diagnosis, which based on single cycle with multiple features, for detecting pulmonary arterial hypertension associated with congenital heart disease. It is a non-invasive and simple method which may be hopeful at early diagnosis of CHD-PAH. The original heart sounds were pre- processed first, in which a double-threshold adaptive segmentation method was used to segment the signal into each cardiac cycle first. Then the time–frequency domain features and wavelet packet energy features of cardiac cycle and S2 component are extracted. And convolutional neural network (CNN) is used to extract the depth features of cardiac cycle. The above features were combined into a fused feature vector. Normal, CHD and CHD-PAH were classified using XGBoost as the classifier. Finally, the majority voting algorithm is used to obtain the best classification result for multiple results corresponding to multiple cardiac cycles of the same person. Using this new method, a classification accuracy of 88.61% was achieved.