TY - GEN
T1 - An Empirical Study of Questionnaires for the Diagnosis of Pediatric Obstructive Sleep Apnea
AU - Ahmed, Sadia
AU - Hasani, Sona
AU - Koone, Mary
AU - Thirumuruganathan, Saravanan
AU - Diaz-Abad, Montserrat
AU - Mitchell, Ron
AU - Isaiah, Amal
AU - Das, Gautam
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - 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.
AB - 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.
KW - machine learning
KW - pediatric sleep apnea
KW - questionnaires
UR - http://www.scopus.com/inward/record.url?scp=85056624369&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056624369&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8513389
DO - 10.1109/EMBC.2018.8513389
M3 - Conference contribution
C2 - 30441257
AN - SCOPUS:85056624369
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4097
EP - 4100
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -