ArticlePloS one2016
A Cough-Based Algorithm for Automatic Diagnosis of Pertussis.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 3 syntheses or guidelines pooled it, 161 citations in OpenAlex.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Guideline
- Evaluating smartphone-based cough frequency monitoring for tuberculosis screening and triage in Uganda: A mixed-methods evaluation.PLOS global public health · 2026Article
- Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.BMC infectious diseases · 2025Article
- Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm.Scientific reports · 2025Article
- Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.JMIR research protocols · 2024Article
- COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers.IEEE journal of biomedical and health informatics · 2024Article
- Identifying unique spectral fingerprints in cough sounds for diagnosing respiratory ailments.Scientific reports · 2024Article
- Feature evaluation of accelerometry signals for cough detection.Frontiers in digital health · 2024Article
- Differences in acoustic features of cough by pneumonia severity in patients with COVID-19: a cross-sectional study.ERJ open research · 2023Article
- An evolutionary ensemble learning for diagnosing COVID-19 via cough signals.Intelligent medicine · 2023Article
- HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.Frontiers in digital health · 2023Review
- Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms.IEEE journal of translational engineering in health and 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
- Enabling effective breathing sound analysis for automated diagnosis of lung diseases.Smart health (Amsterdam, Netherlands) · 2022Article
- Wide-Bandwidth Nanocomposite-Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues.Experimental biology and medicine (Maywood, N.J.) · 2022Review
- Towards sound based testing of COVID-19-Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge.Computer speech & language · 2022Article
- Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.