Enabling Intelligent Resuscitation: Non-Invasive Cardiac Output Monitoring via Physiological Sensing and Machine Learning

Demet Tangolar, Onur Selim Kilic, Samuel Liu, Cem Okan Yaldiz, Jacob P. Kimball, Omer T. Inan

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Keywords: Hemodynamic monitoring, hypovolemia, resuscitation, cardiac output, stroke volume, seismocardiogram

TL;DR: We developed a non-invasive, physiological sensing approach leveraging machine learning to accurately estimate cardiac output during hemorrhage and resuscitation without needing normalization, validated on a porcine model.

Abstract: Accurate, continuous monitoring of cardiac output (CO) is crucial for effective resuscitation management in hemorrhagic trauma, yet current gold-standard methods are invasive and impractical in field settings. This study introduces a fully non-invasive and wearable sensing-based approach utilizing electrocardiography (ECG), seismocardiography (SCG), and photoplethysmography (PPG) signals, integrated with machine learning algorithms, to enable stroke volume (SV) and CO estimation without requiring baseline calibration or normalization. This critical feature makes the model especially suitable for casualty care scenarios where baseline measurements are often unavailable. The proposed methodology was evaluated on a porcine model (n=$6$) subjected to controlled hemorrhage and resuscitation protocols. Clinically-validated cardiovascular features were used as inputs for regression models, including linear, ridge, LASSO, random forest, and XGBoost regressors. Among these, the LASSO demonstrated the best performance, achieving a high correlation ($R = 0.79$) and a mean absolute percentage error (MAPE) of $14.31\%$, well within clinically-acceptable limits for non-invasive CO monitoring. The framework reliably tracked SV trends crucial for clinical decision-making during resuscitation scenarios. This work highlights the potential for intelligent, non-invasive CO monitoring systems to improve clinical and trauma care outcomes.

Track: 3. Signal processing, machine learning, deep learning, and decision-support algorithms for digital and computational health

NominateReviewer: Onur Selim Kilic: [e-mail]

Submission Number: 7