公开(公告)号：US20240023886A1

公开(公告)日：2024-01-25

申请号：US18480906

申请日：2023-10-04

Applicant: SOUTH CHINA NORMAL UNIVERSITY

Inventor： Han ZHANG ,  Weiwei ZHU ,  Songbin YE ,  Baoxian YU

IPC: A61B5/00 ,  G16H50/20 ,  G06N20/20 ,  A61B5/11

CPC classification number: A61B5/4818 ,  G16H50/20 ,  G06N20/20 ,  A61B5/1102 ,  A61B5/7203 ,  A61B5/7267

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.

公开(公告)号：US20230043406A1

公开(公告)日：2023-02-09

申请号：US17790445

申请日：2020-11-12

Applicant: SOUTH CHINA NORMAL UNIVERSITY

Inventor： Han ZHANG ,  Weiwei ZHU ,  Songbin YE ,  Baoxian YU

IPC: A61B5/00

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.