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

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.