An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram


Traditional approaches for obstructive sleep apnea (OSA) diagnosis are apt to using multiple channels of physiological signals to detect apnea events by dividing the signals into equal-length segments, which might lead to incorrect apnea event detection and weaken the performance of OSA diagnosis. This paper proposes an automatic-segmentation-based screening approach with the one channel of Electrocardiogram (ECG) signal for OSA subject diagnosis, and the most work of the proposed approach lies in 3 aspects: (i) an automatic signal segmentation algorithm is adopted for signal segmentation rather than the equal-length segmentation rule; (ii) a native median filter is improved for reduction of the surprising RR intervals before signal segmentation; (iii) the designed OSA severity index and additional admission data of OSA suspects are plugged into support vector machine (SVM) for OSA subject diagnosis. A real clinical example from PhysioNet database is provided to validate the proposed approach and a median accuracy of ninety seven.forty onep.c for subject diagnosis is obtained that demonstrates the effectiveness for OSA diagnosis.

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