Open-set lung sound recognition model based on conditional Gaussian capsule network and variational time–frequency feature reconstruction

Abstract

Lung sound auscultation is an essential method for diagnosing lung diseases; however, most existing lung sound recognition methods fail to identify classes that are unknown in training. Thus, we proposed an open-set lung sound recognition model based on the conditional Gaussian capsule network and variational time–frequency feature reconstruction. The proposed model incorporates an inference network, a cubic encoder, an attention module, a classifier, a cubic decoder, and a generative network. First, the inference network is employed to extract the time–frequency features of lung sounds at a single time step. Then, the variational distribution of lung sounds is computed using the capsule network and optimized to approximate the Gaussian model of the class to which the sample belongs according to the labels. Time–frequency synchronized feature extraction and reconstruction are performed on the entire lung sound sample using the cubic encoder and cubic decoder. Finally, we utilize the generative network to refactor the lung sound features for open-set recognition. The proposed model was evaluated experimentally on a combined dataset using two different category assignment schemes. The results demonstrate that the proposed model achieved accuracies of 82.31% and 88.47%, respectively, thereby outperforming existing methods.