Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence

There are no seroprevalence terms in the subcorpus

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    Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound MESHD Data

    Authors: Chloƫ Brown; Jagmohan Chauhan; Andreas Grammenos; Jing Han; Apinan Hasthanasombat; Dimitris Spathis; Tong Xia; Pietro Cicuta; Cecilia Mascolo

    id:2006.05919v2 Date: 2020-06-10 Source: arXiv

    Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease MESHD or assess disease progression MESHD. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs MESHD coughs HP. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds MESHD collected to aid diagnosis of COVID-19. We use coughs MESHD coughs HP and breathing to understand how discernible COVID-19 sounds are from those in asthma MESHD asthma HP or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough MESHD cough HP from a healthy user with a cough MESHD cough HP, and users who tested positive for COVID-19 and have a cough MESHD cough HP from users with asthma MESHD asthma HP and a cough MESHD cough HP. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.

    Coswara -- A Database of Breathing, Cough MESHD Cough HP, and Voice Sounds for COVID-19 Diagnosis

    Authors: Neeraj Sharma; Prashant Krishnan; Rohit Kumar; Shreyas Ramoji; Srikanth Raj Chetupalli; Nirmala R.; Prasanta Kumar Ghosh; Sriram Ganapathy

    id:2005.10548v1 Date: 2020-05-21 Source: arXiv

    The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough MESHD cough HP and breathing difficulties. We foresee that respiratory sounds MESHD, when analyzed using machine learning techniques, can provide useful insights, enabling the design of a diagnostic tool. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds MESHD, namely, cough MESHD cough HP, breath, and voice. The sound samples are collected via worldwide crowdsourcing using a website application. The curated dataset is released as open access. As the pandemic is evolving, the data collection and analysis is a work in progress. We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection MESHD, and in the near future this can help to diagnose COVID-19.

    Coswara -- A Database of Breathing, Cough MESHD Cough HP, and Voice Sounds for COVID-19 Diagnosis

    Authors: Neeraj Sharma; Prashant Krishnan; Rohit Kumar; Shreyas Ramoji; Srikanth Raj Chetupalli; Nirmala R.; Prasanta Kumar Ghosh; Sriram Ganapathy

    id:2005.10548v2 Date: 2020-05-21 Source: arXiv

    The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough MESHD cough HP and breathing difficulties. We foresee that respiratory sounds MESHD, when analyzed using machine learning techniques, can provide useful insights, enabling the design of a diagnostic tool. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds MESHD, namely, cough MESHD cough HP, breath, and voice. The sound samples are collected via worldwide crowdsourcing using a website application. The curated dataset is released as open access. As the pandemic is evolving, the data collection and analysis is a work in progress. We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection MESHD, and in the near future this can help to diagnose COVID-19.

    The respiratory sound MESHD features of COVID-19 patients fill gaps between clinical data and screening methods

    Authors: Ying hui Huang; Si jun Meng; Yi Zhang; Shui sheng Wu; Yu Zhang; Ya wei Zhang; Yi xiang Ye; Qi feng Wei; Nian gui Zhao; Jian ping Jiang; Xiao ying Ji; Chun xia Zhou; Chao Zheng; Wen Zhang; Li zhong Xie; Yong chao Hu; Jian quan He; Jian Chen; Wang yue Wang; Chang hua Zhang; Liming Cao; Wen Xu; Yunhong Lei; Zheng hua Jian; Wei ping Hu; Wen juan Qin; Wan yu Wang; Yu long He; Hang Xiao; Xiao fang Zheng; Yi Qun Hu; Wen Sheng Pan; Jian feng Cai

    doi:10.1101/2020.04.07.20051060 Date: 2020-04-10 Source: medRxiv

    Background: The 2019 novel coronavirus (COVID-19) has continuous outbreaks around the world. Lung is the main organ that be involved. There is a lack of clinical data on the respiratory sounds MESHD of COVID-19 infected pneumonia MESHD pneumonia HP, which includes invaluable information concerning physiology and pathology. The medical resources are insufficient, which are now mainly supplied for the severe patients. The development of a convenient and effective screening method for mild or asymptomatic TRANS suspicious patients is highly demanded. Methods: This is a retrospective case series study. 10 patients with positive results of nucleic acid were enrolled in this study. Lung auscultation was performed by the same physician on admission using a hand-held portable electronic stethoscope delivered in real time via Bluetooth. The recorded audio was exported, and was analyzed by six physicians. Each physician individually described the abnormal breathing sounds that he heard. The results were analyzed in combination with clinical data. Signal analysis was used to quantitatively describe the most common abnormal respiratory sounds MESHD. Results: All patients were found abnormal breath sounds HP at least by 3 physicians, and one patient by all physicians. Cackles, asymmetrical vocal resonance and indistinguishable murmurs are the most common abnormal breath sounds HP. One asymptomatic TRANS patient was found vocal resonance, and the result was correspondence with radiographic computed tomography. Signal analysis verified the credibility of the above abnormal breath sounds HP. Conclusions: This study describes respiratory sounds MESHD of patients with COVID-19, which fills up for the lack of clinical data and provides a simple screening method for suspected patients.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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