Detection and Diagnostic Approach of COVID-19 Based on Cough Sound Analysis (original) (raw)
2021, Journal of Computer Science
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Screening of COVID-19 using Cough Audio Frequencies
International Journal of Advanced Trends in Computer Science and Engineering , 2021
Clinicians routinely use biomedical and audio signals (e.g. sighs, breathing, pulse, digestion, sounds of vibration) as markers to diagnose diseases or to evaluate the progression of diseases. Until recently, these signals were normally obtained during scheduled visits by manual auscultation. With the advancement of technologies, digital methods are used to collect the body sounds for cardiovascular or respiratory testing (e.g. digital stethoscopes to predict the progression of diseases. A few early studies showed promising results for the detection of COVID-19 using voice and diagnostic signals. In the proposed model, an effective analysis is performed through the collection of large, multi-group, airborne acoustic sound data to perform the screening of COVID-19. The technique uses cough and breathing patterns to show the distinctive features of COVID-19 and it is reported that the cough patterns of COVID-19 are identifiable from asthma cough patterns. Using machine learning algorithms, an efficient classification model is developed for the screening of COVID-19.The area below the curve (AUC) of our proposed model exceeds 80%. The present study also explores the analysis of air patterns that can be recorded using the breathing styles of the infected persons to enhance the efficiency of the proposed screening techniques.
PeerJ Computer Science
For COVID-19, the need for robust, inexpensive, and accessible screening becomes critical. Even though symptoms present differently, cough is still taken as one of the primary symptoms in severe and non-severe infections alike. For mass screening in resource-constrained regions, artificial intelligence (AI)-guided tools have progressively contributed to detect/screen COVID-19 infections using cough sounds. Therefore, in this article, we review state-of-the-art works in both years 2020 and 2021 by considering AI-guided tools to analyze cough sound for COVID-19 screening primarily based on machine learning algorithms. In our study, we used PubMed central repository and Web of Science with key words: (Cough OR Cough Sounds OR Speech) AND (Machine learning OR Deep learning OR Artificial intelligence) AND (COVID-19 OR Coronavirus). For better meta-analysis, we screened for appropriate dataset (size and source), algorithmic factors (both shallow learning and deep learning models) and corr...
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