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Isfahan Artificial Intelligence Event 2024, Challenge I: Respiratory Depression Detection Publisher



Kenari AR ; Esmaeili N ; Tajmirriahi M ; Khashei M ; Ebrahimpour M ; Alinezhad P ; Sheikhi E ; Loghmani A ; Torabi MR ; Abruee M ; Kiani M ; Nekouei F ; Fakhar MY ; Saghaei M Show All Authors
Authors
  1. Kenari AR
  2. Esmaeili N
  3. Tajmirriahi M
  4. Khashei M
  5. Ebrahimpour M
  6. Alinezhad P
  7. Sheikhi E
  8. Loghmani A
  9. Torabi MR
  10. Abruee M
  11. Kiani M
  12. Nekouei F
  13. Fakhar MY
  14. Saghaei M
  15. Moradi MH
  16. Rabbani H

Source: Journal of Medical Signals and Sensors Published:2026


Abstract

Background: The use of sedative drugs during various medical procedures is on the rise, necessitating close monitoring of respiratory function throughout the administration process. Continuous auscultation of tracheal sounds is an effective method for monitoring respiratory status. However, it requires constant attention from the operator, which may not always be feasible. Methods: This concept led to the development of a tracheal sound dataset featuring recordings from 16 patients who underwent cataract surgery at Alzahra Hospital, a university hospital in Isfahan, Iran. To ensure accuracy, the dataset was carefully examined with the assistance of an anesthesiology team, providing precise ground truth annotations for respiratory depression (RD) intervals at a resolution of one second. The Isfahan National Elite Foundation hosted the Isfahan artificial intelligence (AI) 2024 events to advance AI-based detection technologies and offered financial support for five challenges, including the competition for detecting RD from tracheal sounds. Twelve teams from various provinces across Iran participated, utilizing a shared dataset for their evaluations. Results: The teams that achieved the first through third places were Houshmandsazan, Houshava, and Hoopad, with F1-Scores of 65.18%, 50.44%, and 21.73%, respectively. All participating teams utilized deep learning techniques to detect RD intervals, achieving notable performance, yet opportunities for further improvement remain. Conclusion: This paper summarizes the performance of these teams, detailing the metrics used to assess their results and the methodologies employed by the top three competitors. © 2026 Journal of Medical Signals & Sensors.