Real-Time Sleep Apnea Detection by Classifier Combination

To find an efficient and valid alternative of polysomnography (PSG), this paper investigates real-time sleep apnea and hypopnea syndrome (SAHS) detection based on electrocardiograph (ECG) and saturation of peripheral oxygen (SpO 2 ) signals, individually and in combination. We include ten machine-le...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2012-05, Vol.16 (3), p.469-477
Main Authors: Xie, B., Hlaing Minn
Format: Article
Language:English
Subjects:
Accuracy
Adult
Aged
Artificial Intelligence
Classifier combination
Computer Systems
electrocardiograph (ECG)
Electrocardiography
Electrocardiography - methods
Feature extraction
feature selection
Female
Humans
hypopnea
Indexes
machine learning
Male
Middle Aged
Monitoring, Physiologic - methods
Oximetry - methods
Real time systems
saturation of peripheral oxygen (SpO _2 )
Sensitivity
Signal Processing, Computer-Assisted
sleep apnea
Sleep Apnea Syndromes - diagnosis
Sleep Apnea Syndromes - physiopathology
Sleep disorders
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Summary:To find an efficient and valid alternative of polysomnography (PSG), this paper investigates real-time sleep apnea and hypopnea syndrome (SAHS) detection based on electrocardiograph (ECG) and saturation of peripheral oxygen (SpO 2 ) signals, individually and in combination. We include ten machine-learning algorithms in our classification experiment. It is shown that our proposed SpO 2 features outperform the ECG features in terms of diagnostic ability. More importantly, we propose classifier combination to further enhance the classification performance by harnessing the complementary information provided by individual classifiers. With our selected SpO 2 and ECG features, the classifier combination using AdaBoost with Decision Stump, Bagging with REPTree, and either kNN or Decision Table achieves sensitivity, specificity, and accuracy all around 82% for a minute-based real-time SAHS detection over 25 sleep-disordered-breathing suspects' full overnight recordings.
ISSN:1089-7771
2168-2194
1558-0032
2168-2208
DOI:10.1109/TITB.2012.2188299