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.

翻译：在许多临床假想情况中,例如监测2019年科朗病毒病的疾病发展趋势,以手持听诊器取代常规听力仪,但实际应用中尚未验证一个强大的计算机化呼吸声分析算法。在这项研究中,我们开发了一个肺声数据库(HF_Lung_V1),包括9 765个肺声音档案(每部15个),34 095个吸入标签,18 349个排泄标签,13 883个连续的冒险声(CAS)标签(包括8 457个heise标签,686个特里多标签,4 740个Rhonchi标签),以及15 606个不连续的冒险声频标签(所有裂痕)。 我们开发了一个长短期记忆基准测试(LSTM),以GRIMTM为主、双向直线路基的RSTM模型(BIRIM-RMS), 最不连续的神经神经-内、最直径的RIMIS-RIMRR(RRRR)的运行模型, 最新阶段,以及S-RIS-RIS-RIS-SLS) 最接近的运行阶段的运行模型。