A physiological status diagnosis method using tensor-based regularization - An, Yu; Chen, Shanen; Zhang, Xi (IEEE International Conference on Automation Science and Engineering (CASE), 2022)
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.