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

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.

arXiv admin note: text overlap with arXiv:2112.07285

References in corpus (7)

• AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App
• Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
• The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms
• Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner
• Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device
• Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough
• Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC

Cited by in corpus (4)

• Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice
• On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection
• Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation
• Detection of Disease on Nasal Breath Sound by New Lightweight Architecture: Using COVID-19 as An Example