Systolic blood pressure estimation using ECG and PPG in patients undergoing surgery. (January 2023)

• Record Type: Journal Article
• Authors: Sun, Shaoxiong; Bresch, Erik; Muehlsteff, Jens; Schmitt, Lars; Long, Xi; Bezemer, Rick; Paulussen, Igor; Noordergraaf, Gerrit J.; Aarts, Ronald M.
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Blood pressure -- Photoplethysmography (PPG) -- Electrocardiography (ECG) -- Surgery -- Dynamic feature selection -- Multiple linear regression -- Pulse arrival time (PAT); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104040
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Systolic blood pressure was estimated every 30 s using PPG and ECG in patients undergoing surgery. Nine features capturing information embedded in waveforms and derivatives were extracted from PPG and ECG signals. Dynamic feature selection was proposed based on feature robustness and the principle of correlation-based feature selection. The proposed method achieved high estimation accuracy meeting the AAMI standard. Abstract: 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.