Respiratory Sound Classification Based on BiGRU-Attention Network with XGBoost

Abstract

In recent years, the mortality rate of respiratory diseases ranks high among the major diseases. Early detection of respiratory diseases is a key factor in reducing the mortality rate and curing diseases. In this paper, we propose the BiGRU Attention-XGBoost model to classify respiratory sounds, in order to assist doctors in the early diagnosis of respiratory diseases. Specifically, we first extract two sets of features, i.e., the time domain and spectral features to encode respiratory sounds. Then, we apply the Gradient Boosting Decision Tree algorithm to select important features for classification. Based on the temporal characteristics of respiratory sounds, we design the BiGRU Attention-XGBoost model to classify them. Finally, to enlarge the training dataset and address the problem of data imbalance, we also implement Griffifin-Lim and WORLD Vocoder, two data augmentation methods. Extensive experiments show the superiority of the proposed model compared to seven state-of the-art models in terms of classification accuracy and Fl-score.