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arXiv · 2307.05096

The smarty4covid dataset and knowledge base: a framework enabling interpretable analysis of audio signals

Konstantia Zarkogianni·Edmund Dervakos·George Filandrianos·Theofanis Ganitidis·Vasiliki Gkatzou·Aikaterini Sakagianni·Raghu Raghavendra·C. L. Max Nikias·Giorgos Stamou·Konstantina S. Nikita
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Abstract

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL) knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (ii) identify cough, breath and voice segments in crowd-sourced audio recordings. A new framework utilizing the smarty4covid OWL knowledge base towards generating counterfactual explanations in opaque AI-based COVID-19 risk detection models is proposed and validated.

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Konstantia Zarkogianni, Edmund Dervakos, George Filandrianos, Theofanis Ganitidis, Vasiliki Gkatzou, Aikaterini Sakagianni, Raghu Raghavendra, C. L. Max Nikias, Giorgos Stamou, Konstantina S. Nikita. 2023-07-11. The smarty4covid dataset and knowledge base: a framework enabling interpretable analysis of audio signals. https://arxiv.org/abs/2307.05096

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