Yuyang Gao ¹ Pengyue Ma ¹ Jiahua Pan ² Hongbo Yang ² Tao Guo ² Weilian Wang ^1*

• ¹ School of Information Science and Engineering, Yunnan University, Kunming, Yunnan Province, China
• ² Yunnan Fuwai Cardiovascular Hospital, Kunming, Yunnan Province, China

Objective: Congenital heart disease with pulmonary arterial hypertension (CHD-PAH), caused by CHD, is associated with high clinical mortality. Hence, timely diagnosis is imperative for treatment.Approach: Two non-invasive diagnosis algorithms of CHD-PAH were put forward in this review, which were direct three-divided and two-stage classification models. Pre-processing in both algorithms focuses on segmentation of heart sounds into discrete cardiac cycles. Both the dualthreshold and Bi-LSTM (Bi-directional Long Short-Term Memory) methods demonstrate efficacy. In the feature extraction phase, the direct three-divided model integrate time-, frequency-, and energydomain features with deep learning features. While the two-stage classification model sequentially extracts sub-band envelopes and short-time energy of cardiac cycle. In the classification phase, considering the lack of CHD-PAH data, ensemble learning was widely used.Main results: An accuracy of 88.61% was achieved with direct three-divided model and 90.9% with two-stage classification model.Significance: By analyzing and discussing these algorithms, future research directions of CHD-PAH assisted diagnosis were discussed. It is hoped that it will provide insight into prediction of CHD-PAH. Thus saving people from death due to untimely assistance.