ArticleSN computer science2021
Robust Detection of COVID-19 in Cough Sounds: Using Recurrence Dynamics and Variable Markov Model.
Article in SN computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 114 citations in OpenAlex.
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- The Use of Audio Signals for Detecting COVID-19: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Cough Audio Recognition for Early Detection of Respiratory Diseases: Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers.Scientific reports · 2025Article
- Cough duration, energy and sound frequency in COVID-19 patients: the spectral analysis results.BMC pulmonary medicine · 2025Article
- Cough2COVID-19 detection using an enhanced multi layer ensemble deep learning framework and CoughFeatureRanker.Scientific reports · 2024Article
- An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio.Bioengineering (Basel, Switzerland) · 2024Article
- Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.Frontiers in public health · 2024Article
- Feature evaluation of accelerometry signals for cough detection.Frontiers in digital health · 2024Article
- Review
- Analysis of retail sector research evolution and trends during COVID-19.Technological forecasting and social change · 2023Article
- Article
- Differences in acoustic features of cough by pneumonia severity in patients with COVID-19: a cross-sectional study.ERJ open research · 2023Article
- Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.Biomedical signal processing and control · 2023Article
- COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- An evolutionary ensemble learning for diagnosing COVID-19 via cough signals.Intelligent medicine · 2023Article
- Cough Audio Analysis for COVID-19 Diagnosis.SN computer science · 2023Article
- Identifying health research in the era of COVID-19: A scoping review.SAGE open medicine · 2023Article
- COVID-19 assessment using HMM cough recognition system.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023Article
- Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms.IEEE journal of translational engineering in health and medicine · 2023Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19, otherwise known as the coronavirus, has precipitated the world into a pandemic that has infected, as of the time of writing, more than 10 million persons worldwide and caused the death of more than 500,000 persons. Early symptoms of the virus include trouble breathing, fever and fatigue and over 60% of people experience a dry cough. Due to the devastating impact of COVID-19 and the tragic loss of lives, it is of the utmost urgency to develop methods for the early detection of the disease that may help limit its spread as well as aid in the development of targeted solutions. Coughs and other vocal sounds contain pulmonary health information that can be used for diagnostic purposes, and recent studies in chaotic dynamics have shown that nonlinear phenomena exist in vocal signals. The present work investigates the use of symbolic recurrence quantification measures with MFCC features for the automatic detection of COVID-19 in cough sounds of healthy and sick individuals. Our performance evaluation reveals that our symbolic dynamics measures capture the complex dynamics in the vocal sounds and are highly effective at discriminating sick and healthy coughs. We apply our method to sustained vowel 'ah' recordings, and show that our model is robust for the detection of the disease in sustained vowel utterances as well. Furthermore, we introduce a robust novel method of informative undersampling using information rate to deal with the imbalance in our dataset, due to the unavailability of an equal number of sick and healthy recordings. The proposed model achieves a mean classification performance of 97% and 99%, and a mean
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