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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 basisof Lung_V2. However, the performance of CAS detection was poor due to manyreasons, one of which is the highly diversified CAS patterns. To make theoriginal CNN-BiGRU model learn the CAS patterns more effectively and not causetoo much computing burden, three strategies involving minimal modifications ofthe network architecture of the CNN layers were investigated: (1) making theCNN layers a bit deeper by using the residual blocks, (2) making the CNN layersa bit wider by increasing the number of CNN kernels, and (3) separating thefeature input into multiple paths (the model was denoted by Multi-pathCNN-BiGRU). The performance of CAS segment and event detection were evaluated.Results showed that improvement in CAS detection was observed among all theproposed architecture-modified models. The F1 score for CAS event detection ofthe proposed models increased from 0.445 to 0.491-0.530, which was deemedsignificant. However, the Multi-path CNN-BiGRU model outperformed the othermodels in terms of the number of winning titles (five) in total nine evaluationmetrics. In addition, the Multi-path CNN-BiGRU model did not cause extracomputing burden (0.97-fold inference time) compared to the original CNN-BiGRUmodel. Conclusively, the Multi-path CNN layers can efficiently improve theeffectiveness of feature extraction and subsequently result in better CASdetection.

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