Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 60, Issue 8, Pages 2350-2360Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2013.2256423
Keywords
Cardiovascular model; ECG-derived activation functions; virtual cardiovascular instrument
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Funding
- National Science Foundation [CMMI-0729552, CMMI-0830023]
- ATT Professorship
- Vietnam Education Foundation
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1437139] Funding Source: National Science Foundation
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We present an approach to deriving a real-time, lumped parameter cardiovascular dynamics model that uses features extracted from online electrocardiogram (ECG) signal recordings to generate certain surrogate hemodynamic signals. The model represents the coupled dynamics of the heart chambers, valves, and pulmonary and systemic blood circulation loops in the form of nonlinear differential equations. The features extracted from ECG signals were used to estimate the timings and amplitudes of the atrioventricular activation input functions as well as other model parameters that capture the effect of cardiac morphological and physiological characteristics. The model was tested using hemodynamic signals from the PhysioNet MGH/MF Waveform database. The results suggest that the model can capture the salient time and frequency patterns of the measured central venous pressure, pulmonary arterial pressure, and respiratory impedance signals (R-2 > 0.65). We have developed a method based on Anderson-Darling statistic and Kullback-Leibler divergence to compare the clinical measures (i.e., systolic and diastolic pressures) estimated from model waveform-extrema with those from actual measurements. The test statistics of the model waveform-extrema were statistically indistinguishable from the measured values with beat-to-beat rejection rates of 10%. The results indicate the potential of a virtual instrument that uses the model-derived signals for clinical diagnosis in lieu of expensive instrumentation.
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