Systolic blood pressure estimation using ECG and PPG in patients undergoing surgery

Shaoxiong Sun (Corresponding author), Erik Bresch, Jens Muehlsteff, Lars Schmitt, Xi Long, Rick Bezemer, Igor W.F. Paulussen, Gerrit J. Noordergraaf, Ronald M. Aarts

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
124 Downloads (Pure)


Background and Objectives: In a significant portion of surgeries, blood pressure (BP) is often measured non-invasively in an intermittent manner. This practice has a risk of missing clinically relevant BP changes between two adjacent intermittent BP measurements. This study proposes a method to non-invasively estimate systolic blood pressure (SBP) with high accuracy in patients undergoing surgery. Methods: Continuous arterial BP, electrocardiography (ECG), and photoplethysmography (PPG) signals were acquired from 29 patients undergoing surgery. After extracting 9 features from the PPG and ECG signals, we dynamically selected features upon each intermittent measurement (every 10 min) of SBP based on feature robustness and the principle of correlation-based feature selection. Finally, multiple linear regression models were built to combine these features to estimate SBP every 30 s. Results: Compared to the reference SBP, the proposed method achieved a mean of difference at 0.08 mmHg, a standard deviation of difference at 7.97 mmHg, and a correlation coefficient at 0.89 (p < 0.001). Conclusions: This study demonstrates the feasibility of non-invasively estimating SBP every 30 s with high accuracy during surgery by using ECG, PPG, and intermittent SBP measurements every 10 min, which meets the standard of the Association for the Advancement of Medical Instrumentation. The proposed method has the potential to enhance BP monitoring in the operating room, improving patient outcomes and experiences.

Original languageEnglish
Article number104040
Number of pages10
JournalBiomedical Signal Processing and Control
Issue numberPart 1
Publication statusPublished - Jan 2023


  • blood pressure
  • photoplethysmography
  • electrocardiogram
  • surgery
  • feature selection
  • multiple linear regression
  • pulse arrival time
  • Dynamic feature selection
  • Multiple linear regression
  • Pulse arrival time (PAT)
  • Electrocardiography (ECG)
  • Photoplethysmography (PPG)
  • Surgery
  • Blood pressure


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