A physiological status diagnosis method using tensor-based regularization

• Published in: IEEE International Conference on Automation Science and Engineering (CASE) pp. 943 - 948
• Main Authors: An, Yu, Chen, Shanen, Zhang, Xi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20.08.2022
• Subjects: Automation; Brain; Computer aided software engineering; Convex functions; Optimization; Physiology; Tensors
• ISSN: 2161-8089
• DOI: 10.1109/CASE49997.2022.9926554

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.