Characteristics of Pulmonary Auscultation in Patients with 2019 Novel Coronavirus in China

Wang, Bo; Liu, Yanbin; Wang, Ye; Yin, Wei Hsian; Liu, Tao; Liú, Dan; Li, Diandian; Feng, Mei; Zhang, Yanlin; Liang, Zongan; Fu, Ziqiao; Fu, Siyun; Li, Weimin; Xiong, Nian; Wang, Gang; Luo, Fengming

doi:10.1159/000509610

Cited by 25 publications

(25 citation statements)

References 20 publications

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“…Third, prolonged continuous monitoring of lung sound is almost impractical. Lastly, a practitioner wearing personal protective equipment finds it difficult to perform auscultation without breaching the protection [8], which limits the use of auscultation on a patient with an airborne or droplet-transmitted pulmonary disease, such as coronavirus disease 2019 (COVID-19) [9][10][11]. To overcome the aforementioned limitations, computerized respiratory sound analysis [12] is required.…”

Section: Introductionmentioning

confidence: 99%

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

Hsu

Huang²,

Huang³

et al. 2021

PLoS ONE

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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.

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Section: Introductionmentioning

confidence: 99%

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

Hsu

Huang²,

Huang³

et al. 2021

PLoS ONE

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“…2), an abnormal nature was usually observed by COVID-19 subjects, while healthy subjects had a more regular pattern during breathing. This could be related to the hidden characteristics of COVID-19 contaminated within lungs and exhibited during lung inhale and exhale [47][48][49]. Additionally, the MFCC transformation of these recordings (Fig.…”

Section: Smartphone-based Breathing Recordingsmentioning

confidence: 89%

“…Many studies have investigated the information carried by respiratory sounds in patients tested positive for COVID-19 [16][17][18]. Furthermore, it has been found that vocal patterns extracted from COVID-19 patients' speech recordings carry indicative biomarkers for the existence of the viral infection [19].…”

Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 in smartphone-based breathing recordings using CNN-BiLSTM: a pre-screening deep learning tool

Alkhodari

Khandoker

2021

Preprint

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This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein the relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.

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“…Such findings are subject to observer variation however, and the absence of such signs does not denote absence of disease. 35 …”

Section: Methodsmentioning

confidence: 99%

Improving the early identification of COVID-19 pneumonia: a narrative review

et al. 2021

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Delayed presentation of COVID-19 pneumonia increases the risk of mortality and need for high-intensity healthcare. Conversely, early identification of COVID-19 pneumonia grants an opportunity to intervene early and thus prevent more complicated, protracted and less successful hospital admissions. To improve the earlier detection of COVID-19 pneumonia in the community we provide a narrative review of current evidence examining the clinical parameters associated with early disease progression. Through an evolving literature review, we examined: the symptoms that may suggest COVID-19 progression; the timing of deterioration; the utility of basic observations, clinical examination and chest X-ray; the value of postexertion oxygen saturations; and the use of CRP to monitor disease progression. We go on to discuss the challenges in monitoring the COVID-19 patient in the community and discuss thresholds for further assessment. Confusion, persistent fever and shortness of breath were identified as worrying symptoms suggestive of COVID-19 disease progression necessitating urgent clinical contact. Importantly, a significant proportion of COVID-19 pneumonia patients appear not to suffer dyspnoea despite severe disease. Patients with this asymptomatic hypoxia seem to have a poorer prognosis. Such patients may present with other signs of hypoxia: severe fatigue, exertional fatigue and/or altered mental status. We found duration of symptoms to be largely unhelpful in determining risk, with evidence of deterioration at any point in the disease. Basic clinical parameters (pulse, respiratory rate, blood pressure, temperature and oxygen saturations (SpO2)) are likely of high value in detecting the deteriorating community COVID-19 patient and/or COVID-19 mimickers/complications (eg, sepsis, bacterial pneumonia and pulmonary embolism). Of these, SpO2 carried the greatest utility in detecting COVID-19 progression. CRP is an early biochemical parameter predictive of disease progression and used appropriately is likely to contribute to the early identification of COVID-19 pneumonia. Identifying progressive COVID-19 in the community is feasible using basic clinical questions and measurements. As such, if we are to limit the mortality, morbidity and the need for complicated, protracted admissions, monitoring community COVID-19 cases for signs of deterioration to facilitate early intervention is a viable strategy.

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Characteristics of Pulmonary Auscultation in Patients with 2019 Novel Coronavirus in China

Cited by 25 publications

References 20 publications

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

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

Detection of COVID-19 in smartphone-based breathing recordings using CNN-BiLSTM: a pre-screening deep learning tool

Improving the early identification of COVID-19 pneumonia: a narrative review

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