Application of Acoustic Sensing in Systemic to Pulmonary Shunts in Ductal Dependent Infants Using Deep Learning

Congenital heart defects (CHDs) are birth defects that change the heart’s structure and blood flow. Patients with these conditions may require an artificially placed shunt to allow adequate circulation through the heart and lungs. Evaluating shunt function is paramount to sustaining the functionality and life of the shunt. Effective monitoring of the shunt requires noninvasive approaches to enable frequent monitoring of the shunt’s condition. In this work, we propose deep learning architectures for the classification of heart sounds associated with blood flow through shunts, obtained using a digital stethoscope from infants with ductal-dependent physiology. Specifically, we propose a convolutional neural network-long short-term memory model for shunt-type classification. In addition, a variational autoencoder model utilizing latent space representation addresses five clinically relevant tasks: 1) determining flow status during extracorporeal membrane oxygenation (ECMO); 2) discriminating flow patterns before and after shunt or pulmonary artery angioplasty; 3) identifying cyanotic conditions versus noncyanotic states; 4) change of flow dynamics over time; and 5) assessing flow status under elevated pulmonary artery pressure. Experimental results showcase the effectiveness of our approach, with the shunt-type classification achieving an F1 score of 0.88 and an area under the ROC curve (AUC) of 0.95. For the remaining tasks (1)–(5), support vector machine, random forest, and k-nearest neighbors models are trained using the latent space representations, yielding AUC values of 0.77, 0.72, 0.85, 0.60, and 0.81, respectively. This development demonstrates that the acoustic signals captured via a stethoscope may contain valuable information for monitoring shunt flow changes in patients with CHD.