Abstract: Disclosed are a method, device, computer system for obtaining respiratory signal. The method includes: filtering aliasing vital signs signals obtained by piezoelectric sensor to obtain target vital signs signals; performing a Fourier transform to obtain first frequency response, and generating an upper envelope according to each frequency point of the first frequency response; determining main peak frequency point and main peak amplitude according to the frequency point corresponding to the maximum value of flat tops; identifying the flat top corresponding to the main peak amplitude as main peak flat top; determining minimum value frequency point according to the minimum value between the main peak flat top and an adjacent flat top or flat bottom; and determining respiratory spectrum principal component interval according to the minimum value frequency point; reconstructing the respiratory spectrum principal component interval through an empirical wavelet function to obtain reconstructed respiratory signal.

Abstract: A noninvasive method and system for sleep apnea detection. The noninvasive method for sleep apnea detection comprises: collecting vital sign signals of a sleeping user; performing structured processing on the vital sign signals of the sleeping user to remove invalid signals to obtain a set of valid vital sign signals; extracting multi-dimensional morphological features from a sleep respiratory signal and performing feature training on multiple initial models of a classifier by means of the multi-dimensional morphological features to obtain a sleep breathing detection model; and inputting the set of valid vital sign signals into the sleep breathing detection model and performing signal processing to obtain probability data of the sleeping user experiencing sleep apnea; inputting the set of valid vital sign signals into a sleep breathing detection model and performing signal processing to obtain predicted probability of the sleeping user suffering from sleep apnea.

Abstract: A noninvasive method and system for sleep apnea detection is disclosed. The method includes the following steps: acquiring vital sign signals of a sleeping user; performing structured processing on the vital sign signals of the user to remove invalid signals to obtain a set of valid vital sign signals; extracting multi-dimensional morphological features from a sleep respiratory signal and performing feature training on an initial model of a classifier by means of the multi-dimensional morphological features so as to obtain a sleep breathing detection model; and inputting the set of valid vital sign signals into the sleep breathing detection model and performing signal processing to obtain predicted probability of the user suffering from sleep apnea. As a result, data relating to the probability of a user suffering from sleep apnea can be more accurately obtained, thereby facilitating the determination of whether a sleep apnea event occurs during sleep.