A Comparative Study of Signal Representations Methods and Deep Learning Architectures for PPG-Based Cuffless Blood Pressure Estimation.
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| Title: | A Comparative Study of Signal Representations Methods and Deep Learning Architectures for PPG-Based Cuffless Blood Pressure Estimation. |
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| Authors: | Zhang H; School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China., Hu X; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China.; Ningbo Ming Sing Optical R&D Co., Ltd., Ningbo 315104, China., Zhang X; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China., Chen Z; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China.; Guangxi Key Laboratory of Metabolic Reprogramming and Intelligent Medical Engineering for Chronic Diseases, Guilin 541004, China., Liang Y; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China.; Guangxi Key Laboratory of Metabolic Reprogramming and Intelligent Medical Engineering for Chronic Diseases, Guilin 541004, China., Wang G; The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China. |
| Source: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 May 02; Vol. 26 (9). Date of Electronic Publication: 2026 May 02. |
| Publication Type: | Journal Article; Comparative Study |
| Journal Info: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1424-8220 |
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| DOI: | 10.3390/s26092847 |
