Development and validation of a deep learning-based model for estimating stroke volume and blood pressures simultaneously using electrocardiogram and photoplethysmogram.

Background: Cardiac output (CO) is essential for systemic oxygen delivery, but gold-standard measurement using pulmonary artery catheter thermodilution remains invasive. Arterial pressure-based cardiac output (APCO) devices are widely used but still require arterial catheterization. We developed and validated a deep learning (DL) model that simultaneously estimates stroke volume (SV), systolic blood pressure (SBP), and diastolic blood pressure (DBP) using electrocardiogram (ECG) and photoplethysmogram (PPG). Methods: We retrospectively analyzed 881 surgical cases at Seoul National University Hospital (Aug 2016 - Dec 2020) with simultaneous ECG, PPG, arterial blood pressure waveform (ABP), SV measured by the APCO devices, and demographics. After signal-quality rules and arterial-line plausibility filters, 20-s ECG/PPG segments were paired SV to form samples. A DL model using ECG, PPG, the first and second derivatives of PPG, demographic variables, and ECG-derived features simultaneously estimated SV, SBP, and DBP. Model performance was evaluated in a temporal hold-out cohort using error metrics, Bland-Altman analysis, and 10-min interval trend analysis. SBP and DBP performance was assessed against ISO 81060-3:2022 criteria. Results: The test set comprised 128 cases (31,637 segments). Our DL model achieved a mean absolute error of 10.761 mL/beat and percentage limits of agreement ranging from -26.905% to 29.563%. SBP and DBP showed mean errors of 0.661 and 1.439 mmHg, with standard deviations of 9.716 and 6.684 mmHg, respectively, meeting the ISO 81060-3:2022 criteria. Conclusion: A DL model using ECG/PPG and demographics can estimate SV with clinically useful performance while simultaneously predicting SBP/DBP, potentially expanding continuous hemodynamic monitoring to non-arterial-line cases.