Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds

Abstract

Testing capacity for COVID-19 remains a challenge globally due to the lack of adequate supplies, trained personnel, and sample-processing equipment. These problems are even more acute in rural and underdeveloped regions. We demonstrate that solicited-cough sounds collected over a phone, when analysed by our AI model, have statistically significant signal indicative of COVID-19 status (AUC 0.72, t-test,p <0.01,95% CI 0.61-0.83). This holds true for asymptomatic patients as well. Towards this, we collect the largest known(to date) dataset of microbiologically confirmed COVID-19 cough sounds from 3,621 individuals. When used in a triaging step within an overall testing protocol, by enabling risk-stratification of individuals before confirmatory tests, our tool can increase the testing capacity of a healthcare system by 43% at disease prevalence of 5%, without additional supplies, trained personnel, or physical infrastructure

Under submission to AAAI 20

References in corpus (4)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
• Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis
• An Overview on Audio, Signal, Speech, & Language Processing for COVID-19

Cited by in corpus (14)

• COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings
• A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels
• Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data
• Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough
• Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition
• End-2-End COVID-19 Detection from Breath & Cough Audio
• Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19
• COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation
• An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification
• COVID-19 Detection Using Recorded Coughs in the 2021 DiCOVA Challenge
• Recent Advances in Computer Audition for Diagnosing COVID-19: An Overview
• Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
• SRIB Submission to Interspeech 2021 DiCOVA Challenge
• Diagnosis of COVID-19 and Non-COVID-19 Patients by Classifying Only a Single Cough Sound