The Use of Audio Signals for Detecting COVID-19: A Systematic Review

Sensors (Basel). 2022 Oct 23;22(21):8114. doi: 10.3390/s22218114.

Abstract

A systematic review on the topic of automatic detection of COVID-19 using audio signals was performed. A total of 48 papers were obtained after screening 659 records identified in the PubMed, IEEE Xplore, Embase, and Google Scholar databases. The reviewed studies employ a mixture of open-access and self-collected datasets. Because COVID-19 has only recently been investigated, there is a limited amount of available data. Most of the data are crowdsourced, which motivated a detailed study of the various pre-processing techniques used by the reviewed studies. Although 13 of the 48 identified papers show promising results, several have been performed with small-scale datasets (<200). Among those papers, convolutional neural networks and support vector machine algorithms were the best-performing methods. The analysis of the extracted features showed that Mel-frequency cepstral coefficients and zero-crossing rate continue to be the most popular choices. Less common alternatives, such as non-linear features, have also been proven to be effective. The reported values for sensitivity range from 65.0% to 99.8% and those for accuracy from 59.0% to 99.8%.

Keywords: affordable healthcare; audio-based health assessment; automatic COVID-19 diagnosis; contactless health monitoring; digital health; remote health monitoring.

Publication types

  • Systematic Review
  • Review

MeSH terms

  • Algorithms
  • COVID-19* / diagnosis
  • Databases, Factual
  • Humans
  • Neural Networks, Computer
  • Support Vector Machine

Grants and funding

This research received no external funding.