Apnea medassist: Real-time sleep apnea monitor using single-lead ECG
Abstract
We have developed a low-cost, real-time sleep apnea monitoring system ''Apnea MedAssist" for recognizing obstructive sleep apnea episodes with a high degree of accuracy for both home and clinical care applications. The fully automated system uses patient's single channel nocturnal ECG to extract feature sets, and uses the support vector classifier (SVC) to detect apnea episodes. "Apnea MedAssist" is implemented on Android operating system (OS) based smartphones, uses either the general adult subject-independent SVC model or subject-dependent SVC model, and achieves a classification F-measure of 90% and a sensitivity of 96% for the subject-independent SVC. The real-time capability comes from the use of 1-min segments of ECG epochs for feature extraction and classification. The reduced complexity of "Apnea MedAssist" comes from efficient optimization of the ECG processing, and use of techniques to reduce SVC model complexity by reducing the dimension of feature set from ECG and ECG-derived respiration signals and by reducing the number of support vectors.
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For ECG signals, although Bsoul et al. proposed dozens of features, the main focus of research is to consider R points and their properties such as R-R intervals and pick counts.
“As an example, for the works studying ECG signals, although Bsoul et al. [65] have proposed dozens of features, the main focus of research is to consider R points (the pick point of each beat), and its properties in ECG pulses such as R-R intervals, pick counts, etc. [39,64].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [65]
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- Authors
- Bsoul, M., Minn, H., & Tamil, L.
- Affiliations
- The University of Texas at Dallas (US)
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2011-05
- Volume
- 15
- Issue
- 3
- Pages
- 416-427
- ISSN
- 1089-7771 (ISSN-L)
- 1558-0032
- OpenAlex
- W2108691203
- Cited by
- 297
- References
- 33
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 9 · 2023: 20 · 2022: 23 · 2021: 32 · 2020: 23 · 2019: 22 · 2018: 22 · 2017: 19 · 2016: 24 · 2015: 21 · 2014: 21 · 2013: 20 · 2012: 14
- FWCI
- 8.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Asian Institute of Technology
- 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
- obstructive sleep apnea, sleep apnea detection, single-lead ECG, ECG-derived respiration, support vector classifier, smartphone-based monitoring, real-time monitoring, low-cost medical device, feature extraction, dimensionality reduction, support vector reduction, home health monitoring
- MeSH terms
- Adult, Algorithms, Electrocardiography, Female, Humans, Male, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Sleep Apnea Syndromes, Reproducibility of Results, Polysomnography
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