Deep Learning-Based Cardiac Output Estimation Using Multimodal Physiological Signals

Cardiac output (CO) is one of the most significant physiological parameters for cardiovascular monitoring. The gold standard for CO estimation, thermodilution, requires invasive catheterization, limiting its frequent use. Non-invasive methods exist but often lack accuracy. In this study, we applied deep learning to estimate CO based on different input signals including arterial pressure (ART), electrocardiography (ECG), photoplethysmography (PPG), and their combinations. Using publicly available VitalDB database, models were trained and evaluated according to different signals. Performance was measured using mean absolute error (MAE), root mean square error (RMSE), bias, and limits of agreement (LOA). Of all combinations tested, the triadic model of ART, ECG, and PPG yielded the best performance in MAE (0.66 L/min) and stronger correlation (R = 0.84) with reference CO values. The present study indicates the promise of deep learning for accurate noninvasive estimation of CO. Future research should emphasize the interpretability of the model, scaling up datasets, and facilitating real time applications for increased clinical utility.