Cough-based COVID-19 detection with audio quality clustering and confidence measure based learning

Alice Ashby, Julia Meister, Khuong Nguyen, Zhiyuan Luo, Werner Gentzke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

COVID-19 cough classification has rapidly become a promising research avenue as an accessible and low-cost screening alternative, needing only a smartphone to collect and process cough samples. However, audio processing of recordings made in uncontrolled environments and prediction confidence are key challenges that need to be addressed before cough-screening could be widely accepted as a trusted testing method.

Therefore, we propose a novel approach for cough event detection that identifies cough clusters instead of individual coughs, significantly reducing onset detection's usual hypersensitivity to energy fluctuations between cough phases.

By using this technique to improve training sample quality and quantity by +200%, we improve Machine Learning performance on the minority COVID-19 class by up to 20%, achieving up to +47% precision and +15% recall. We propose a novel, class-agnostic Conformal Prediction non-conformity measure which takes the cough sample quality into account to counteract the variance caused by limiting segmentation to just the training set. Our Conformal Prediction model introduces uncertainty quantification to COVID-19 cough classification and achieves an additional 34% improvement to precision and recall.
Original languageEnglish
Title of host publicationProceedings of Machine Learning Research
Subtitle of host publication11th Symposium on Conformal and Probabilistic Prediction with Applications
EditorsUlf Johansson, Henrik Bostrom, Khuong An Nguyen, Zhiyuan Luo, Lars Carlsson
Number of pages20
Volume179
Publication statusPublished - Aug 2022

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