Does the sample entropy approach improve the accuracy of detecting sleep apnea compared to spectral analysis in subjects with obstructive sleep apnea syndrome?
Sample entropy offers a computationally simple alternative to spectral analysis for detecting sleep apnea via heart rate variability, though it does not significantly improve diagnostic accuracy on its own.
Sample entropy, a nonlinear signal processing approach, was used as a measure of signal complexity to evaluate the cyclic behavior of heart rate variability (HRV) in obstructive sleep apnea syndrome (OSAS). In a group of 10 normal and 25 OSA subjects, the sample entropy measure showed that normal subjects have significantly more complex HRV pattern than the OSA subjects (p < 0.005). When compared with spectral analysis in a minute-by-minute classification, sample entropy had an accuracy of 70.3% (69.5% sensitivity, 70.8% specificity) while the spectral analysis had an accuracy of 70.4% (71.3% sensitivity, 69.9% specificity). The combination of the two methods improved the accuracy to 72.9% (72.2% sensitivity, 73.3% specificity). The sample entropy approach does not show major improvement over the existing methods. In fact, its accuracy in detecting sleep apnea is relatively low in the well classified data of the physionet. Its main achievement however, is the simplicity of computation. Sample entropy and other nonlinear methods might be useful tools to detect apnea episodes during sleep.
Al-Angari et al. (Mon,) studied this question.