Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features
Redmond B Shouldice1, Louise M O'Brien, Ciara O'Brien
1Digital Signal Processing Research Group, Department of Electronic and Electrical Engineering, University College Dublin, Dublin, Ireland.
Insights
This study shows an automated system using electrocardiogram (ECG) can detect obstructive sleep apnea (OSA) in children. The system achieved high accuracy, suggesting potential for clinical use in pediatric sleep apnea diagnosis.
Area of Science:
- Pediatric Sleep Medicine
- Cardiology
- Biomedical Engineering
Background:
- Obstructive sleep apnea (OSA) is a common condition in children.
- Accurate diagnosis of pediatric OSA typically requires polysomnography, which is resource-intensive.
- Non-invasive, automated diagnostic tools are needed to improve accessibility.
Purpose of the Study:
- To evaluate the feasibility of an automated classification system for detecting pediatric OSA.
- To analyze overnight electrocardiogram (ECG) recordings for OSA detection.
- To assess the diagnostic performance of an ECG-based automated system.
Main Methods:
- Retrospective observational study involving 50 children.
- Overnight polysomnography data was analyzed.
- An automated classification system was developed using modified quadratic discriminant analysis on ECG features.
Main Results:
- The automated system achieved 88% accuracy on training data and 84% accuracy on a withheld test set.
- Sensitivity was 85.7% and specificity was 81.8% on the test dataset.
- The system correctly identified 12 out of 14 OSA cases.
Conclusions:
- The automated ECG analysis system demonstrates potential clinical utility for diagnosing OSA in children.
- This approach may offer a more accessible method for pediatric sleep apnea screening.
- Further validation in larger cohorts is warranted.
Study Objectives:
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.
Design:
Retrospective observational study.
Setting:
A pediatric sleep clinic.
Participants:
Fifty children underwent full overnight polysomnography.
Intervention:
N/A.
Measurements And 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.
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