Real-time sleep apnea detection by classifier combination

Xie, B.; Hlaing Minn

doi:10.1109/titb.2012.2188299

Cited by 294Obstructive Sleep Apnea ResearchNon-Invasive Vital Sign MonitoringECG Monitoring and Analysis

Real-time sleep apnea detection by classifier combination

Xie, B., Hlaing Minn·2012·IEEE Transactions on Information Technology in Biomedicine

DOI 10.1109/titb.2012.2188299

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Abstract

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.

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Recent works in diagnosis/decision making involve estimating severity of health episodes of patients with chronic disease, sleep issues such as polysomnography and apnea, estimation and classification of health conditions, and emotion recognition.
“Some examples of recent works in this area involve estimating the severity of health episodes of patients suffering chronic disease [44–46], sleep issues such as polysomnography and apnea [47,48], estimation and classification of health conditions [49,50], and emotion recognition [51].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [48]

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Authors: Xie, B., & Minn, H.
Affiliations: The University of Texas at Dallas (US)
ORCID: Xie, B.
Hlaing Minn
Venue: IEEE Transactions on Information Technology in Biomedicine
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2012-05
Volume: 16
Issue: 3
Pages: 469-477
ISSN: 1089-7771 (ISSN-L)
1558-0032
DOI: 10.1109/titb.2012.2188299
OpenAlex: W2131111256
Cited by: 294
References: 34
Citations by year: 2026: 8 · 2025: 17 · 2024: 11 · 2023: 27 · 2022: 21 · 2021: 35 · 2020: 31 · 2019: 32 · 2018: 25 · 2017: 26 · 2016: 17 · 2015: 17 · 2014: 9 · 2013: 10 · 2012: 8
FWCI: 9.44
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Physiology › Obstructive Sleep Apnea Research
Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Keywords: sleep apnea detection, sleep apnea-hypopnea syndrome, real-time diagnosis, electrocardiogram, oxygen saturation, classifier combination, ensemble learning, AdaBoost, bagging, REPTree, k-nearest neighbors, decision tables, sleep-disordered breathing, polysomnography alternative
MeSH terms: Adult, Aged, Artificial Intelligence, Computer Systems, Electrocardiography, Female, Humans, Male, Middle Aged, Monitoring, Physiologic, Oximetry, Signal Processing, Computer-Assisted, Sleep Apnea Syndromes

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Real-time sleep apnea detection by classifier combination

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