The smarty4covid dataset and knowledge base as a framework for interpretable physiological audio data analysis

The smarty4covid dataset and knowledge base as a framework for interpretable physiological audio data analysis

Blog Article

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 great value pads 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 forgylt s?lv AI-based COVID-19 risk detection models is proposed and validated.