A literature review on COVID-19 disease diagnosis from respiratory sound data

Kranthi Kumar Lella; Alphonse PJA; Kranthi Kumar Lella; Alphonse PJA

doi:10.3934/bioeng.2021013

AIMS Bioengineering

2021, Volume 8, Issue 2: 140-153. doi: 10.3934/bioeng.2021013

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A literature review on COVID-19 disease diagnosis from respiratory sound data

Kranthi Kumar Lella ,
Alphonse PJA ^,

Department of Computer Applications, NIT Tiruchirappalli, Tamil Nadu, India-620015

Received: 12 January 2021 Accepted: 25 February 2021 Published: 05 March 2021

The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few months. In this particular situation, many techniques, methods, and AI-based classification algorithms are put in the spotlight in reacting to fight against it and reduce the rate of such a global health crisis. COVID-19's main signs are heavy temperature, different cough, cold, breathing shortness, and a combination of loss of sense of smell and chest tightness. The digital world is growing day by day; in this context digital stethoscope can read all of these symptoms and diagnose respiratory disease. In this study, we majorly focus on literature reviews of how SARS-CoV-2 is spreading and in-depth analysis of the diagnosis of COVID-19 disease from human respiratory sounds like cough, voice, and breath by analyzing the respiratory sound parameters. We hope this review will provide an initiative for the clinical scientists and researcher's community to initiate open access, scalable, and accessible work in the collective battle against COVID-19.
- COVID-19,
- World Health Organization,
- machine learning,
- deep learning,
- artificial intelligence,
- respiratory sounds
Citation: Kranthi Kumar Lella, Alphonse PJA. A literature review on COVID-19 disease diagnosis from respiratory sound data[J]. AIMS Bioengineering, 2021, 8(2): 140-153. doi: 10.3934/bioeng.2021013

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Abstract

The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few months. In this particular situation, many techniques, methods, and AI-based classification algorithms are put in the spotlight in reacting to fight against it and reduce the rate of such a global health crisis. COVID-19's main signs are heavy temperature, different cough, cold, breathing shortness, and a combination of loss of sense of smell and chest tightness. The digital world is growing day by day; in this context digital stethoscope can read all of these symptoms and diagnose respiratory disease. In this study, we majorly focus on literature reviews of how SARS-CoV-2 is spreading and in-depth analysis of the diagnosis of COVID-19 disease from human respiratory sounds like cough, voice, and breath by analyzing the respiratory sound parameters. We hope this review will provide an initiative for the clinical scientists and researcher's community to initiate open access, scalable, and accessible work in the collective battle against COVID-19.

Conflict of interest

The authors declared no conflict of interest.

Author contributions

Kranthi Kumar Lella: Original draft preparation, Formal Analysis, and Data Conceptualization. Alphonse PJA: Supervision, Reviewing, and Editing.

References

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