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

Journal: Scientific data
PMID:

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.

Authors

  • Konstantia Zarkogianni
  • Edmund Dervakos
    National Technical University of Athens, School of Electrical and Computer Engineering, Athens, 157 80, Greece.
  • George Filandrianos
    National Technical University of Athens, School of Electrical and Computer Engineering, Athens, 157 80, Greece.
  • Theofanis Ganitidis
  • Vasiliki Gkatzou
    National Technical University of Athens, School of Electrical and Computer Engineering, Athens, 157 80, Greece.
  • Aikaterini Sakagianni
    Sismanogleio General Hospital, Intensive Care Unit, Marousi, Greece.
  • Raghu Raghavendra
    University of Southern California, Viterbi School of Engineering, Los Angeles, 90089, USA.
  • C L Max Nikias
    University of Southern California, Viterbi School of Engineering, Los Angeles, 90089, USA.
  • Giorgos Stamou
    School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece.
  • Konstantina S Nikita
    3 School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., 15780 Zografos, Athens, Greece.