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Obstructive sleep apnea detection using SVM-based classification of ECG signal features.
Laiali Almazaydeh1, Khaled Elleithy, Miad Faezipour
1Department of Computer Science and Engineering, University of Bridgeport, Brodgeport, CT 06604, USA. lalmazay@bridgeport.edu
Summary
A new automated system accurately detects sleep apnea using electrocardiogram (ECG) data. This advanced technique offers a highly accurate and convenient alternative to traditional sleep studies for obstructive sleep apnea (OSA) screening.
Area of Science:
- Biomedical Engineering
- Cardiology
- Sleep Medicine
Background:
- Sleep apnea involves breathing pauses during sleep, with Obstructive Sleep Apnea (OSA) being the most common form.
- Current diagnosis via Polysomnography (PSG) is costly and inconvenient, requiring overnight stays and expert observation.
Purpose of the Study:
- To develop and evaluate an automated classification algorithm for sleep apnea detection.
- To process electrocardiogram (ECG) data for efficient and comfortable sleep disorder identification.
Main Methods:
- Utilized an automated classification algorithm processing short epochs of ECG data.
- Employed Support Vector Machines (SVM) for training and testing the classification model.
- Tested the system on sleep apnea recordings from subjects with and without OSA.
Main Results:
- The automated system achieved a high accuracy of 96.5% or greater in recognizing sleep disorder epochs.
- Demonstrated the system's capability to accurately classify sleep apnea from ECG data.
Conclusions:
- The proposed automated classification system provides a highly accurate method for sleep apnea detection.
- This system can serve as a foundation for developing future tools for OSA screening.
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