Cardiac Volume Signal

Cardiac volume signal (CVS) analysis aims to non-invasively and continuously monitor hemodynamic function, primarily using electrical impedance tomography. Current research heavily focuses on developing robust machine learning methods, particularly employing unsupervised sequence-to-sequence learning (like LSTM-VAEs) and discriminative models, to automatically assess CVS signal quality and identify motion artifacts that degrade data accuracy. This automated quality assessment is crucial for improving the reliability and clinical utility of real-time hemodynamic monitoring, leading to more informed clinical decisions and reduced resource utilization.