Empirically formulated hypothetical arterial blood pressure waveforms were simulated and assumed to be unknown. Oscillometric pulsations corresponding to these simulated unknown waveforms were used as input to a new model-based extended Kalman filter algorithm for identifying the levels, shape and features of the unknown waveforms.
Simulations with hypothetical waveforms with varying systolic and diastolic levels and with variations in heartbeat rhythm associated arterial fibrillation and stiff arteries, demonstrate potential arterial pressure estimate accuracies of 1.5 mmHg with standard deviations on the order of 0.1 mmHg; in addition, variations in heartbeat rhythm were observed and degrees of arterial stiffness identified and quantified.
Computational analysis of the oscillometric cuff pulsations with the extended Kalman filter can be used to detect variations in heartbeat rhythm and blood pressure levels associated atrial fibrillation, quantify arterial stiffness, and provide noninvasive continuous blood pressure monitoring without the need for electrocardiogram or photoplethysmography sensors.
Copyright © 2020. Published by Elsevier B.V.