Intraoperative blood pressure management plays a critical role in preventing postoperative complications, including organ dysfunction. While oscillometric intermittent noninvasive techniques are the standard for blood pressure monitoring in most surgical patients, they may fail to capture rapid hemodynamic fluctuations, especially during high-risk periods such as induction, intubation, and sudden positioning changes. In these cases, continuous invasive arterial blood pressure (IABP) monitoring is often used for real-time hemodynamic surveillance. However, IABP monitoring carries risks, including bleeding, thrombosis, hematoma, arterial occlusion, and bloodstream infections. Reported radial artery thrombosis rates reach as high as 19.7%, and catheter-related complications such as hematoma or bleeding occur in up to 15% of cases.
There is an increasing demand for noninvasive continuous blood pressure monitoring (NCBPM) technologies to avoid such complications. Photoplethysmography (PPG), derived from pulse oximeters, has emerged as a promising noninvasive technique. PPG waveforms reflect peripheral perfusion changes and offer insights into cardiac cycle dynamics. However, standalone PPG-based estimation is limited by its dependency on peripheral vascular tone, motion artifacts, and lack of electrical cardiac data.
To overcome these limitations, this study proposes a hybrid approach using synchronized photoplethysmography (PPG) and electrocardiography (ECG) signals, enhanced with artificial intelligence (AI)-based waveform analysis. This combination aims to improve the accuracy and temporal resolution of noninvasive continuous blood pressure monitoring. The proposed method, termed CPES (Combined Pulse Oximeter and ECG Signals), is based on the hypothesis that integrating electrical (ECG) and optical (PPG) signals will allow better modeling of arterial pulse wave velocity (PWV), pulse transit time (PTT), and surrogate hemodynamic parameters.
The study design includes intraoperative real-time recording of PPG and ECG signals in patients undergoing elective surgery under general anesthesia with concurrent IABP monitoring via radial artery catheterization. PPG waveforms will be acquired using a standard red/infrared dual-wavelength pulse oximeter (660 nm and 940-960 nm), and ECG will be collected using 3-lead continuous monitoring. Synchronization will be ensured using a time-stamping algorithm. Data acquisition will occur during distinct intraoperative phases: baseline (pre-induction), induction, intubation, maintenance, and positional interventions (±15° Trendelenburg and reverse Trendelenburg).
The primary outcome is the agreement between CPES-derived systolic and diastolic blood pressure values and simultaneously recorded invasive blood pressure values, assessed via Bland-Altman analysis. Clinical accuracy will be determined by comparing mean absolute error (MAE) and root mean square error (RMSE) to the AAMI/ANSI/ISO 81060-2 standard, which requires a mean difference of ≤5 mmHg and standard deviation of ≤8 mmHg.
Secondary outcomes include:
Ability of the CPES system to track rapid blood pressure changes (ΔSBP, ΔDBP) during induction and airway manipulation.
Evaluation of positional effects on blood pressure estimation accuracy using paired positional comparisons.
Exploratory modeling of derived hemodynamic parameters (e.g., PTT, HRV, PWV) and their correlations with arterial wave morphology and invasive BP metrics.
Signal processing will involve artifact removal, beat detection, normalization, and segmentation. Feature extraction will include time-domain, frequency-domain, and morphological features from both PPG and ECG. Machine learning models (e.g., gradient boosting, LSTM, or convolutional neural networks) may be trained on the dataset to enhance predictive performance.
This study aims to demonstrate that combining electrical and optical signals can yield clinically acceptable blood pressure measurements noninvasively, improving patient safety and reducing the need for invasive lines. If validated, this approach could be integrated into existing operating room monitors, expanding access to continuous BP monitoring in routine and resource-limited settings.
