Cough activity detection for automatic tuberculosis screening
Joshua Jansen van V\"uren, Devendra Singh Parihar, Daphne Naidoo, Kimsey Zajac, Willy Ssengooba, Grant Theron, Thomas Niesler

TL;DR
This study demonstrates that pre-trained transformer models, particularly XLS-R, can accurately detect cough activity in audio recordings, enabling scalable tuberculosis screening tools with high precision and low computational requirements.
Contribution
The paper introduces the use of pre-trained transformer architectures for cough activity detection, highlighting the effectiveness of XLS-R with reduced layers for mobile health applications.
Findings
XLS-R achieves 0.96 average precision and 0.99 AUC in cough detection.
Using only the first three layers of XLS-R reduces computational load.
Coughs isolated by XLS-R improve downstream TB classification performance.
Abstract
The automatic identification of cough segments in audio through the determination of start and end points is pivotal to building scalable screening tools in health technologies for pulmonary related diseases. We propose the application of two current pre-trained architectures to the task of cough activity detection. A dataset of recordings containing cough from patients symptomatic for tuberculosis (TB) who self-present at community-level care centres in South Africa and Uganda is employed. When automatic start and end points are determined using XLS-R, an average precision of 0.96 and an area under the receiver-operating characteristic of 0.99 are achieved for the test set. We show that best average precision is achieved by utilising only the first three layers of the network, which has the dual benefits of reduced computational and memory requirements, pivotal for smartphone-based…
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Taxonomy
TopicsRespiratory and Cough-Related Research · COVID-19 diagnosis using AI · Pneumonia and Respiratory Infections
