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Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals.

Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless c...

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Published in:BioMed Research International pp. 1 - 17
Main Authors: Qin, Caijie, Wang, Xiaohua, Xu, Guangjun, Ma, Xibo
Format: equations & formulas research systematic review tables/charts Journal Article
Published: Wiley-Blackwell 10/1/2022
Online Access:View this record in EBSCOhost
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      dt: 10/1/2022
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        159430756
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        10.1155/2022/8094351
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        atl: Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals.
      aug:
        au:
          Qin, Caijie
          Wang, Xiaohua
          Xu, Guangjun
          Ma, Xibo
        affil: Institute of Information Engineering, Sanming University, Sanming, China
      sug:
        subj:
          Blood Pressure Determination Methods
          Plethysmography Methods
          Human
          Systematic Review
          PubMed
          Artificial Intelligence
          Algorithms
      ab: Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        systematic review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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