Intelligent Bio-Impedance System for Personalized Continuous Blood Pressure Measurement

Creators: Wang, Ting-Wei; Syu, Jhen-Yang; Chu, Hsiao-Wei; Sung, Yen-Ling; Chou, Lin; Escott, Endian; Escott, Olivia; Lin, Ting-Tse; Lin, Shien-Fong

Abstract

Continuous blood pressure (BP) measurement is crucial for long-term cardiovascular monitoring, especially for prompt hypertension detection. However, most of the continuous BP measurements rely on the pulse transit time (PTT) from multiple-channel physiological acquisition systems that impede wearable applications. Recently, wearable and smart health electronics have become significant for next-generation personalized healthcare progress. This study proposes an intelligent single-channel bio-impedance system for personalized BP monitoring. Compared to the PTT-based methods, the proposed sensing configuration greatly reduces the hardware complexity, which is beneficial for wearable applications. Most of all, the proposed system can extract the significant BP features hidden from the measured bio-impedance signals by an ultra-lightweight AI algorithm, implemented to further establish a tailored BP model for personalized healthcare. In the human trial, the proposed system demonstrates the BP accuracy in terms of the mean error (ME) and the mean absolute error (MAE) within 1.7 ± 3.4 mmHg and 2.7 ± 2.6 mmHg, respectively, which agrees with the criteria of the Association for the Advancement of Medical Instrumentation (AAMI). In conclusion, this work presents a proof-of-concept for an AI-based single-channel bio-impedance BP system. The new wearable smart system is expected to accelerate the artificial intelligence of things (AIoT) technology for personalized BP healthcare in the future.

Additional Information

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Received: 25 January 2022 / Revised: 25 February 2022 / Accepted: 26 February 2022 / Published: 28 February 2022. (This article belongs to the Section Biosensors and Healthcare). This research was funded by Ministry of Science and Technology, Taiwan under funding number MOST 110-2917-I-564-026, MOST 110-2628-B-002-055, and MOST 109-2628-B-002-033; University-Industry Collaboration (National Yang Ming Chiao Tung University and Leadtek Research Inc.) under Grant 109A159. Author Contributions. T.-W.W. conceived of the presented idea, supervised the experiment's progress, and wrote the manuscript. J.-Y.S. developed artificial intelligence software and data analysis. H.-W.C. and L.C. implemented the system and performed the human trials and validation. Y.-L.S. performed the data analysis and graphing. E.E. and O.E. performed investigation and visualization. T.-T.L. provided the resources and funding. S.-F.L. supervised the project and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of National Yang Ming Chiao Tung University (approval numbers: NCTU-REC-109-012E). Informed consent was obtained from all subjects involved in the study. The authors declare no conflict of interest.

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