https://scholars.lib.ntu.edu.tw/handle/123456789/607544
標題: | Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a selfdeveloped open-access lung sound database-HF_Lung_V1 | 作者: | Hsu F.-S FEI-PEI LAI et al. |
關鍵字: | abnormal respiratory sound;adult;adventitious sound detection;aged;Article;audio file;benchmarking;bidirectional gated recurrent unit model;bidirectional long short term memory model;breath phase detection;clinical article;continuous adventitious sound label;controlled study;convolutional neural network bidirectional gated recurrent unit model;convolutional neural network bidirectional long short term memory model;convolutional neural network gated recurrent unit model;convolutional neural network long short term memory model;crackle;data base;data processing;diagnostic accuracy;diagnostic test accuracy study;discontinuous adventitious sound label;exhalation label;female;gated recurrent unit model;human;inhalation label;intermethod comparison;long short term memory model;lung sound database;male;model;predictive value;receiver operating characteristic;recurrent neural network;respiratory tract parameters;rhonchus;sensitivity and specificity;sound analysis;sound detection;stridor;wheezing;breathing;diagnosis;disease exacerbation;factual database;lung;middle aged;pathophysiology;very elderly;Adult;Aged;Aged, 80 and over;Benchmarking;COVID-19;Databases, Factual;Disease Progression;Female;Humans;Lung;Male;Middle Aged;Neural Networks, Computer;Respiration;Respiratory Sounds | 公開日期: | 2021 | 卷: | 16 | 期: | 7 | 來源出版物: | PLoS ONE | 摘要: | 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 for breath phase detection and adventitious sound detection at the recording level 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 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using 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. ? 2021 Hsu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110382476&doi=10.1371%2fjournal.pone.0254134&partnerID=40&md5=fae8b5ec690cfa592d0050ee9ac58a0c https://scholars.lib.ntu.edu.tw/handle/123456789/607544 |
ISSN: | 19326203 | DOI: | 10.1371/journal.pone.0254134 |
顯示於: | 生醫電子與資訊學研究所 |
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