Screening of obstructive sleep apnea with empirical mode decomposition of pulse oximetry

Abstract

Detection of desaturations on the pulse oximetry signal is of great importance for the diagnosis of sleep apneas. Using the counting of desaturations, an index can be built to help in the diagnosis of severe cases of obstructive sleep apnea-hypopnea syndrome. It is important to have automatic detection methods that allows the screening for this syndrome, reducing the need of the expensive polysomnography based studies. In this paper a novel recognition method based on the empirical mode decomposition of the pulse oximetry signal is proposed. The desaturations produce a very specific wave pattern that is extracted in the modes of the decomposition. Using this information, a detector based on properly selected thresholds and a set of simple rules is built. The oxygen desaturation index constructed from these detections produces a detector for obstructive sleep apnea-hypopnea syndrome with high sensitivity (0.838) and specificity (0.855) and yields better results than standard desaturation detection approaches.

Nocturia in Patients With Sleep-Disordered Breathing and Cardiovascular Disease

“…However, previous studies reported that the ODI was useful in OSA because it closely correlated with the AHI and had potential for facilitating the accurate diagnosis of OSA with high sensitivity and specificity. 48, 49 Moreover, a previous clinical study demonstrated that the cut-off value to predict an AHI of >5, >15, and >30 events/h with high accuracy was an ODI of >5, >15, and >30 events/h, respectively. 50 Third, patients were diagnosed with sleep apnea according to the AHI based on polysomnography, whereas the AHI during CPAP was extracted from a smart card attached to the device, and the 2 AHIs were compared: before and during CPAP treatment.…”

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“…Azarbarzin and Moussavi [97] developed a CAD method based on LDA where zero-crossing rate and peak frequency were calculated from snoring sound signals. Schlotthauer et al [98] used empirical mode decomposition of pulse oximetry signals for automated OSAS screening. Varon et al [99] proposed principal components of the QRS complexes as features for detecting OSA.…”

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