A domain transfer based data augmentation method for automated respiratory classification

Automated auscultation has become a hot topic in the medical field for diagnostic and predictive analytic. Automated auscultation aims to improve the classification of respiratory sounds recorded by electronic stethoscope. Researchers have paid great effort on developing intelligent auscultation methods to improve the effectiveness of hearing and assist clinicians, especially deep neural network techniques have been employed in recent years. The performance of deep neural network (DNN) based methods is highly data-dependent. Unfortunately, even the current world's largest publicly available respiratory sound dataset, ICBHI, has only 6898 respiratory cycles with a total length of only 5.5 hours, which become a bottleneck for further improvement of DNN models. Therefore, we propose a data augmentation method for respiratory sounds classification, where the input transformation and migration are implemented. In addition, the classical pipeline which is usually used in the computer vision area is also improved in this work. Experimental results show that the proposed data augmentation methods could improve separation performance than the baseline methods. Especially, the proposed data augmentation could be easily implemented in the existing automated auscultation approaches.