Visualization and classification of the heart sounds of patients with pulmonary hypertension

Background. Pulmonary hypertension is a non-curable disease commonly triggered by a preexisting disease, and patients who develop PH usually have poorer prognoses than those who do not. Right heart catheterization is the gold standard in the diagnosis of pulmonary hypertension. However, it is invasive and not without risk. This study thus aims to find a noninvasive method using heart sound classification to screen for PH in the primary care setting.;Methods. This study recruited thirty-two subjects undergoing right heart catheterization in three cardiac centers. The phonocardiogram was processed with time-frequency spectrum analysis and the normalized average Shannon energy to extract the heart sound features. Principal component analysis was performed before designing the artificial neural network. Multilayer perceptron backpropagation neural networks were used to predict the PAP value. All the networks had different layers with different numbers of hidden neurons, and they were all trained with different learning algorithms for 10 runs. A regression analysis of the network response between the network outputs and the corresponding target outputs specified by the PAP value was performed. Of all the different structures, the best and mean performances were compared among the 10 runs for each algorithm.;Results. The six principal components of heart sound features were used in the ANN training. The network using the Resilient Backpropagation algorithm with a log sigmoid transfer function at the two hidden layers, including 10 hidden neurons in each layer and a linear transfer function at the output layer, performed the best among all ANN design structures. It achieved the highest R value of 0.86 between the predicted output and the target output specified by right heart catheterization measured PAP value.;Conclusion. A neural network for human PAP prediction was designed with a promising result. This novel method of cardiopulmonary assessment is expected to lead to the development of an automatic noninvasive device for the high-volume screening of PH.