8/6/2023 0 Comments 5 adventitious breath sounds![]() ![]() Third, prolonged continuous monitoring of lung sound is almost impractical. Comparing auscultation results between individuals and quantifying the sound change by reviewing historical records are difficult tasks. Second, auscultation is a qualitative analysis method. Even experienced clinicians might not have high consensus rates in their interpretations of auscultatory manifestations. First, the interpretation of auscultation results substantially depends on the subjectivity of the practitioners. However, auscultation performed using a conventional handheld stethoscope involves some limitations. In addition, pulmonary pathologies are suspected when the frequency or intensity of respiratory sounds changes or when adventitious sounds, including continuous adventitious sounds (CASs) and discontinuous adventitious sounds (DASs), are identified. During auscultation, breath cycles can be inferred, which help clinical physicians evaluate the patient’s respiratory rate. ![]() The chestpiece of a stethoscope is usually placed on a patient’s chest or back for lung sound auscultation or over the patient’s tracheal region for tracheal sound auscultation. Respiratory auscultation using a stethoscope has long been a crucial first-line physical examination. Therefore, clinical physicians are frequently required to examine respiratory conditions. Respiration is vital for the normal functioning of the human body. This does not alter our adherence to PLOS ONE policies on sharing data and materials.” commissioned to train the deep learning models. CWH and CHC are with Avalanche Computing Inc., whom Heroic Faith Medical Science Co. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: FSH, SRH, YRC, YCL, BFH, YLW, TLT and CTT are full-time employees and CJH, NJL, WLT and YTC are part-time employees of Heroic Faith Medical Science Co. There was no additional external funding received for this study. Ltd, Taipei, Taiwan, freely provided the lung sound recording device (HF-Type-1) for the study and fully sponsored the data labeling and deep learning model training. 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.ĭata Availability: All relevant data are within the manuscript and its Supporting Information files.įunding: Raising Children Medical Foundation, Taiwan, fully funded all of the lung sound collection and contributed the recordings to Taiwan Society of Emergency and Critical Care Medicine. Received: MaAccepted: JPublished: July 1, 2021Ĭopyright: © 2021 Hsu et al. PLoS ONE 16(7):Įditor: Thippa Reddy Gadekallu, Vellore Institute of Technology: VIT University, INDIA (2021) 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. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.Ĭitation: Hsu F-S, Huang S-R, Huang C-W, Huang C-J, Cheng Y-R, Chen C-C, et al. Furthermore, all bidirectional models outperformed their unidirectional counterparts. 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. The results revealed that these models exhibited adequate performance in lung sound analysis. 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. 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. 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). 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. 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. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |