A physiological status diagnosis method using tensor-based regularization

Physiological status diagnosis plays an important role in clinical practice. Different personal information hinders the practical application heavily. To address this issue, we propose a tensor-based physiological status diagnosis approach, fused the subject-variant information with physiological da...

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Bibliographic Details
Published inIEEE International Conference on Automation Science and Engineering (CASE) pp. 943 - 948
Main Authors An, Yu, Chen, Shanen, Zhang, Xi
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.08.2022
Subjects
Automation
Brain
Computer aided software engineering
Convex functions
Optimization
Physiology
Tensors
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ISSN2161-8089
DOI10.1109/CASE49997.2022.9926554

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Summary:Physiological status diagnosis plays an important role in clinical practice. Different personal information hinders the practical application heavily. To address this issue, we propose a tensor-based physiological status diagnosis approach, fused the subject-variant information with physiological data. The subject-variant information guided similarity information matrix is employed to regularize the tensor-based formulation so that the subject-variant information can be appropriately adopted. We proposed an alternating direction method of multipliers (ADMM) inbuilt with the block coordinate descent (BCD) algorithm to solve this formulation. A real-case dataset has been used to validate the proposed diagnosis method, which shows satisfactory results compared with other existing methods.
ISSN:2161-8089
DOI:10.1109/CASE49997.2022.9926554