A One-Step Physiological Status Assessment Method Fusing Subject-Variant Information

• Published in: IEEE transactions on automation science and engineering Vol. 20; no. 3; pp. 1621 - 1632
• Main Authors: An, Yu, Chen, Shanen, Zhang, Xi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain modeling; Channels; data fusion; Datasets; Decomposition; Electroencephalography; Feature extraction; Mathematical analysis; Matrix decomposition; Modelling; Optimization; Physiological status assessment; Physiology; subject-independence; subject-variant information; Tensors
• DOI: 10.1109/TASE.2022.3183589
• ISSN: 1545-5955

The individual physiological difference has been recognized as one of the major problems while assessing subjects using multiple physiological data modeling and fusion techniques. To address this issue, we propose a one-step tensor-based modeling procedure to fuse the subject-variant information and multi-channel physiological data. Specifically, we consider the information similarity of the information matrixes from the tensor decomposition while introducing the subject-variant information, and form a tensor-based optimization problem to achieve the goal of physiological status assessment. To well solve this problem, a tailored alternating direction method of multipliers (ADMM) embedded block coordinate descent (BCD) algorithm has been proposed. Four real-case datasets from different scenarios have been employed to validate our proposed approach, and the performance indicates the superiority compared to several existing methods. Note to Practitioners -The proposed method aims to assess the physiological status by fusing multi-channel physiological data and subject-variant information. To better implement this method, three things are noteworthy. First, the dataset used in the proposed method should contain aligned multi-channel physiological data and subject-variant data, that is, the multi-channel physiological data and the subject-variant data should be correspondingly related. Second, the size of physiological data segments should be moderate due to the limited number of physiological data channels. Third, the initial value of the rank of CANDECOMP/PARAFAC (CP) tensor decomposition, <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>, should be carefully chosen with the contextual knowledge. The value of <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula> is suggested not higher than the number of channels.