title: Identifying individual sleep apnea/hypoapnea epochs using smartphone-based pulse oximetry.
abstract: Sleep apnea, characterized by frequent pauses in breathing during sleep, poses a serious threat to the healthy growth and development of children. Polysomnography (PSG), the gold standard for sleep apnea diagnosis, is resource intensive and confined to sleep laboratories, thus reducing its accessibility. Pulse oximetry alone, providing blood oxygen saturation (SpO2) and blood volume changes in tissue (PPG), has the potential to identify children with sleep apnea. Thus, we aim to develop a tool for at-home sleep apnea screening that provides a detailed and automated 30 sec epoch-by-epoch sleep apnea analysis. We propose to extract features characterizing pulse oximetry (SpO2 and pulse rate variability [PRV], a surrogate measure of heart rate variability) to create a multivariate logistic regression model that identifies epochs containing apnea/hypoapnea events. Overnight pulse oximetry was collected using a smartphone-based pulse oximeter, simultaneously with standard PSG from 160 children at the British Columbia Children's hospital. The sleep technician manually scored all apnea/hypoapnea events during the PSG study. Based on these scores we labeled each epoch as containing or not containing apnea/hypoapnea. We randomly divided the subjects into training data (40%), used to develop the model applying the LASSO method, and testing data (60%), used to validate the model. The developed model was assessed epoch-by-epoch for each subject. The test dataset had a median area under the receiver operating characteristic (ROC) curve of 81%; the model provided a median accuracy of 74% sensitivity of 75%, and specificity of 73% when using a risk threshold similar to the percentage of apnea/hypopnea epochs. Thus, providing a detailed epoch-by-epoch analysis with at-home pulse oximetry alone is feasible with accuracy, sensitivity and specificity values above 73% However, the performance might decrease when analyzing subjects with a low number of apnea/hypoapnea events.
authors.forename: Ainara
authors.initials: A
authors.lastname: Garde
authors.forename: Parastoo
authors.initials: P
authors.lastname: Dekhordi
authors.forename: J Mark
authors.initials: JM
authors.lastname: Ansermino
authors.forename: Guy A
authors.initials: GA
authors.lastname: Dumont
mesh_terms: D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D010092:Oximetry / Q000379:methods*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012890:Sleep; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology; D000068997:Smartphone