Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.

An Update of a Progressively Expanded Database for Automated Lung Sound Analysis

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.