  A continuous real-time respiratory sound automated analysis system is needed
in clinical practice. Previously, we established an open access lung sound
database, HF_Lung_V1, and automated lung sound analysis algorithms capable of
detecting inhalation, exhalation, continuous adventitious sounds (CASs) and
discontinuous adventitious sounds (DASs). In this study, HF-Lung-V1 has been
further expanded to HF-Lung-V2 with 1.45 times of increase in audio files. The
convolutional neural network (CNN)-bidirectional gated recurrent unit (BiGRU)
model was separately trained with training datasets of HF_Lung_V1 (V1_Train)
and HF_Lung_V2 (V2_Train), and then were used for the performance comparisons
of segment detection and event detection on both test datasets of HF_Lung_V1
(V1_Test) and HF_Lung_V2 (V2_Test). The performance of segment detection was
measured by accuracy, predictive positive value (PPV), sensitivity,
specificity, F1 score, receiver operating characteristic (ROC) curve and area
under the curve (AUC), whereas that of event detection was evaluated with PPV,
sensitivity, and F1 score. Results indicate that the model performance trained
by V2_Train showed improvement on both V1_Test and V2_Test in inhalation, CASs
and DASs, particularly in CASs, as well as on V1_Test in exhalation.
