Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features.
OBJECTIVE
To investigate the feasibility of detecting obstructive sleep apnea (OSA) in children using an automated classification system based on analysis of overnight electrocardiogram (ECG) recordings.


METHODS
Retrospective observational study.


METHODS
A pediatric sleep clinic.


METHODS
Fifty children underwent full overnight polysomnography.


METHODS
N/A.


RESULTS
Expert polysomnography scoring was performed. The datasets were divided into a training set of 25 subjects (11 normal, 14 with OSA) and a withheld test set of 25 subjects (11 normal, 14 with OSA). Features, calculated from the ECG of the 25 training datasets, were empirically chosen to train a modified quadratic discriminant analysis classification system. The selected configuration used a segment length of 60 seconds and processed mean, SD, power spectral density, and serial correlation measures to classify segments as apneic or normal. By combining per-segment classifications and using receiver-operator characteristic analysis, a per-subject classifier was obtained that had a sensitivity of 85.7%, specificity of 90.9%, and accuracy of 88% on the training datasets. The same decision threshold was applied to the withheld datasets and yielded a sensitivity of 85.7%, specificity of 81.8%, and accuracy of 84%. The positive and negative predictive values were 85.7% and 81.8%, respectively, on the test dataset.


CONCLUSIONS
The ability to correctly identify 12 out of 14 cases of OSA (with the 2 false negatives arising from subjects with an apnea-hypopnea index less than 10) indicates that the automated apnea classification system outlined may have clinical utility in pediatric patients.
Digital Signal Processing Research Group, Department of Electronic and Electrical Engineering, University College Dublin, Dublin, Ireland.
Redmond B
RB
Shouldice
Louise M
LM
O'Brien
Ciara
C
O'Brien
Philip
P
de Chazal
David
D
Gozal
Conor
C
Heneghan
D000328:Adult; D015992:Body Mass Index; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D019370:Observation; D017286:Polysomnography; D012189:Retrospective Studies; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*