An Empirical Study of Questionnaires for the Diagnosis of Pediatric Obstructive Sleep Apnea

Sadia Ahmed, Sona Hasani, Mary Koone, Saravanan Thirumuruganathan, Montserrat Diaz-Abad, Ron B Mitchell, Amal Isaiah, Gautam Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Pediatric Obstructive Sleep Apnea (OSA) is a chronic disorder characterized by the disruption in sleep due to involuntary and temporary cessation of breathing. Definitive diagnosis of OSA requires an intrusive and expensive approach based on polysomnography where the children spend a night in the hospital under the supervision of a sleep technician. The prevalence of OSA is increasing, making the traditional diagnostic approach prohibitively expensive. There has been increasing interest in designing inexpensive approaches to screen children such as the use of questionnaires. In this paper, we study the efficacy of five widely used and representative questionnaires on their ability to diagnose and stratify OSA. Our experiments show that the diagnostic ability of each of these questionnaires is insufficient for widespread clinical use. Using techniques from data mining, we identify the most informative questions and propose a new questionnaire. We show that machine learning models trained based on the answers to our questionnaire can stratify OSA with higher accuracy.

Original languageEnglish (US)
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4097-4100
Number of pages4
Volume2018-July
ISBN (Electronic)9781538636466
DOIs
StatePublished - Oct 26 2018
Externally publishedYes
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: Jul 18 2018Jul 21 2018

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period7/18/187/21/18

Keywords

  • machine learning
  • pediatric sleep apnea
  • questionnaires

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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    Ahmed, S., Hasani, S., Koone, M., Thirumuruganathan, S., Diaz-Abad, M., Mitchell, R. B., Isaiah, A., & Das, G. (2018). An Empirical Study of Questionnaires for the Diagnosis of Pediatric Obstructive Sleep Apnea. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (Vol. 2018-July, pp. 4097-4100). [8513389] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8513389