公开(公告)号：US20220313909A1

公开(公告)日：2022-10-06

申请号：US17312950

申请日：2020-11-13

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Zedong NIE ,  Jingzhen LI ,  Yuhang LIU

IPC: A61M5/172 ,  G16H40/63 ,  G16H20/17 ,  G16H50/20

Abstract: A closed-loop artificial pancreas system based on a wearable monitoring method is provided. The system includes: a wearable blood glucose monitoring submodule, configured to obtain a blood glucose sensing signal in a noninvasive manner by utilizing a wearable device; a diet and exercise monitoring submodule, configured to obtain diet monitoring data and exercise monitoring data which can cause variations of blood glucose concentration of a subject to be tested; a calculation control submodule, configured to obtain information related to insulin infusion by utilizing a trained deep learning model, the diet monitoring data, and the exercise monitoring data; an insulin infusion submodule, configured to automatically implement insulin infusion; and an effect assessment module, configured to assess an insulin infusion effect, and to feed an assessment result back to the calculation control submodule, such that the calculation control submodule determines whether to update the information related to insulin infusion.

公开(公告)号：US20220313172A1

公开(公告)日：2022-10-06

申请号：US17312946

申请日：2020-11-13

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Zedong NIE ,  Jingzhen LI ,  Yuhang LIU

IPC: A61B5/00 ,  A61B5/349 ,  A61B5/374 ,  A61B5/256 ,  A61B5/291 ,  A61B5/28 ,  A61B5/145 ,  A61B5/0205

Abstract: A prediabetes detection system and method based on combination of electrocardiogram and electroencephalogram information are provided. The system includes: a signal obtaining module, configured to obtain an electrocardiogram signal and an electroencephalogram signal of a user in a noninvasive manner; a feature extraction module, configured to: perform dimension reduction processing on a combined feature set composed of an electrocardiogram feature and an electroencephalogram feature to obtain a plurality of dimension-reduced combined feature sets, and select an electrocardiogram feature and an electroencephalogram feature meeting a preset criteria of correlation by analyzing a correlation between the plurality of dimension-reduced combined feature sets and a blood glucose concentration value to constitute an optimized combined feature set; and a multimodal fusion module, configured to input the optimized combined feature set into a plurality of trained neural network models, to obtain a detection result by fusing results of the plurality of neural networks.