MSCBU-Net for non-invasive blood pressure estimation through PPG signals

Mingyue Wang; Xin Lu; Zhanfang Zhao

doi:10.1117/12.3051378

18 November 2024 MSCBU-Net for non-invasive blood pressure estimation through PPG signals

Mingyue Wang, Xin Lu, Zhanfang Zhao

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030M (2024) https://doi.org/10.1117/12.3051378
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

The technique of measuring blood pressure (BP) through photoplethysmographic (PPG) signals has been widely applied. But this category of methods faces challenges such as the complexity of measurement devices, cumbersome feature selection and inadequate performance. To address these issues, this paper proposes an innovative MSCBU-Net for BP estimation. The method employs an end-to-end solution to predict mean arterial pressure (MAP), diastolic blood pressure (DBP) and systolic blood pressure (SBP) using PPG signal and intermediate continuous arterial blood pressure (ABP). Based on the evaluation of samples from the UCI-BP database, the average absolute errors determined for SBP, DBP and MAP are 4.87mmHg, 2.77mmHg and 2.41mmHg, respectively, demonstrating high accuracy in BP measurement. Furthermore, the method complies with standards set by the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS), indicating its reliability and practicality in clinical applications. In conclusion, the MSCBU-Net model presents an effective BP measurement method with broad prospects for application in the medical field.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingyue Wang, Xin Lu, and Zhanfang Zhao "MSCBU-Net for non-invasive blood pressure estimation through PPG signals", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030M (18 November 2024); https://doi.org/10.1117/12.3051378

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KEYWORDS

Blood pressure

Error analysis

Feature extraction

Signal processing

Biomedical applications

Denoising

Feature selection

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