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Sleep apnea (SA) syndrome is a respiratory disorder that occurs during the sleep. Polysomnography (PSG) has been widely applied by clinicians as a gold standard in the clinical diagnosis of SA syndrome. However, the use of PSG is inconvenient, intrusive, and significantly affects the sleep quality of patient. In this paper, we provide a nonintrusive solution for SA detection. Specifically, a force sensor was employed for the noninvasive vital sign acquisition during the patient’s sleep, where the respiratory signal was extracted adaptively by using the morphological filter. It was observed that the morphological variations before and during the occurrence of the SA events were significant for the SA discrimination. By taking advantage of the differential features with respect to the respiratory signal, the recognition of the SA event was performed using classifiers. For validation, the all-night PSG recordings of 12 volunteers with 8 SA syndrome patients were obtained from the National Clinical Research Center for Respiratory Disease. Numerical results showed that the proposed scheme achieved an averaged accuracy, sensitivity and specificity of 83.67%, 58.57% and 85.13%, respectively, for the SA recognition.

Removing the respiratory signal is a crucial topic to the high-quality ECG. But, not all models are available in the project. The use of digital filtering, signal averaging, adaptive processing and wavelet transform to remove the respiratory interference have some problems. The ICA algorithm for cancellation of respiratory interference is proposed. It is found that this method is more available to reconstruct high-quality ECG and de-noising artifacts comparing with the wavelet transform. Three steps are performed in the paper. From the simulation aspect, and from the evolution for the ability of de-noising the respiratory signal, as well as from reconstructing ECG, the comparisons between the results using ICA and that using wavelet transform are fulfilled. It is shown that the ICA algorithm is more powerful and more effective to de-noising the respiratory signal from ECG, almost not destroying the original ECG.