Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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A Brief Review of Non-Invasive Blood Pressure Estimation Techniques Using Photoplethysmographic Signals

광용적맥파 신호를 활용한 비침습적 혈압 측정 기술의 현황

  • Yujin Cho (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Gahyeon Kim (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Yeeun Kim (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Seongvin Ju (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Minkyu Ahn (School of Computer Science and Electrical Engineering, Handong Global University)
  • 조유진 (한동대학교 전산전자공학부) ;
  • 김가현 (한동대학교 전산전자공학부) ;
  • 김예은 (한동대학교 전산전자공학부) ;
  • 주성빈 (한동대학교 전산전자공학부) ;
  • 안민규 (한동대학교 전산전자공학부)
  • Received : 2024.10.10
  • Accepted : 2024.12.02
  • Published : 2024.12.31
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Abstract

In this study, we conducted a brief review of 254 studies on blood pressure estimation using photoplethysmography (PPG). The analysis revealed a growing trend in research on blood pressure prediction using PPG. Initially, simple methods like regression analysis were commonly used, but recent studies predominantly employ deep learning techniques. Additionally, many studies use other signals in conjunction with PPG for blood pressure estimation. There were numerous cases where PPG signals were measured using wearable devices or custom hardware, and a significant number of studies utilized publicly available datasets. The features used in blood pressure prediction were classified into six categories for analysis, and it was found that the combination of two feature categories was the most common approach. Overall, Temporal features were the most frequently used. Based on these findings, it is suggested that future research on blood pressure estimation using PPG should carefully consider factors such as signal characteristics, preprocessing, and feature extraction when developing models.

Keywords

Acknowledgement

본 연구는 2023년 과학기술정보통신부 및 정보통신기획평가원의 SW중심대학사업 지원을 받아 수행되었음(2023-0-00055).

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