The smarty4covid dataset and knowledge base: a framework enabling interpretable analysis of audio signals
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.
Keywords
Cite
@article{arxiv.2307.05096,
title = {The smarty4covid dataset and knowledge base: a framework enabling interpretable analysis of audio signals},
author = {Konstantia Zarkogianni and Edmund Dervakos and George Filandrianos and Theofanis Ganitidis and Vasiliki Gkatzou and Aikaterini Sakagianni and Raghu Raghavendra and C. L. Max Nikias and Giorgos Stamou and Konstantina S. Nikita},
journal= {arXiv preprint arXiv:2307.05096},
year = {2023}
}
Comments
Submitted for publication in Nature Scientific Data