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

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.