Multi-path Convolutional Neural Networks Efficiently Improve Feature Extraction in Continuous Adventitious Lung Sound Detection

We previously established a large lung sound database, HF_Lung_V2 (Lung_V2). We trained convolutional-bidirectional gated recurrent unit (CNN-BiGRU) networks for detecting inhalation, exhalation, continuous adventitious sound (CAS) and discontinuous adventitious sound at the recording level on the basis of Lung_V2. However, the performance of CAS detection was poor due to many reasons, one of which is the highly diversified CAS patterns. To make the original CNN-BiGRU model learn the CAS patterns more effectively and not cause too much computing burden, three strategies involving minimal modifications of the network architecture of the CNN layers were investigated: (1) making the CNN layers a bit deeper by using the residual blocks, (2) making the CNN layers a bit wider by increasing the number of CNN kernels, and (3) separating the feature input into multiple paths (the model was denoted by Multi-path CNN-BiGRU). The performance of CAS segment and event detection were evaluated. Results showed that improvement in CAS detection was observed among all the proposed architecture-modified models. The F1 score for CAS event detection of the proposed models increased from 0.445 to 0.491-0.530, which was deemed significant. However, the Multi-path CNN-BiGRU model outperformed the other models in terms of the number of winning titles (five) in total nine evaluation metrics. In addition, the Multi-path CNN-BiGRU model did not cause extra computing burden (0.97-fold inference time) compared to the original CNN-BiGRU model. Conclusively, the Multi-path CNN layers can efficiently improve the effectiveness of feature extraction and subsequently result in better CAS detection.