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Chronic Obstructive Pulmonary Disease01:22

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test

Javier Andreu-Perez1,2, Humberto Perez-Espinosa1,3, Eva Timonet4

  • 1School of Computer Science and Electronic Engineering, Faculty of Science and HealthUniversity of Essex Colchester CO4 3SQ U.K.

IEEE Transactions on Services Computing
|August 8, 2022
PubMed
Summary
This summary is machine-generated.

A new AI tool, DeepCough, analyzes cough sounds to detect COVID-19 (Coronavirus Disease-19) with high accuracy. This accessible screening method aids in rapid COVID-19 identification and management.

Keywords:
Deep Learningaudio systemssmart healthcare

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Area of Science:

  • Artificial Intelligence
  • Bioacoustics
  • Epidemiology

Background:

  • The COVID-19 pandemic necessitates accessible, rapid diagnostic tools for effective containment.
  • Previous cough-based detection studies lacked clinical validation and relied on crowdsourcing.
  • A need exists for reliable, point-of-need screening to identify Coronavirus Disease-19 carriers.

Purpose of the Study:

  • To evaluate the performance of a cough sound analysis algorithm for primary COVID-19 screening.
  • To develop and deploy an accessible web-based tool for detecting Coronavirus Disease-19.
  • To assess the accuracy of the DeepCough algorithm in identifying COVID-19 positive cases and severity levels.

Main Methods:

  • Utilized 8,380 clinically validated cough sound samples (2,339 COVID-19 positive, 6,041 negative) confirmed by qRT-PCR.
  • Developed a deep learning model, DeepCough (2D and 3D versions), employing Empirical Mode Decomposition and convolutional neural networks.
  • Deployed the algorithm in a web application prototype named CoughDetect for multi-platform accessibility.

Main Results:

  • Achieved promising Area Under Curve (AUC), sensitivity, and specificity for COVID-19 recognition.
  • Demonstrated an average AUC of [Formula: see text] for classifying three Coronavirus Disease-19 severity levels.
  • The DeepCough algorithm showed significant potential in identifying infection based on cough acoustics.

Conclusions:

  • The DeepCough algorithm and CoughDetect web tool offer a viable, point-of-need primary screening method for COVID-19.
  • This approach can facilitate rapid detection and self-isolation, potentially mitigating the global Coronavirus Disease-19 pandemic.
  • AI-driven analysis of cough sounds presents a scalable solution for infectious disease surveillance.