Babbs, Charles F., "Origin of the electroarthrogram and streaming potentials in compressed cartilage" (2022). Weldon School of Biomedical Engineering Faculty Working Papers. Paper 27.
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biophysics, boundary layer, compression, cyclic loading, dipole, electrical potentials, electromechanical transduction, extracellular matrix, far field, fixed anions, fixed charge density, forward problem, glycosaminoglycans, knee, laminar flow, loading, matrix, osteoarthritis, proteoglycans, stiffness, strain, streaming potential, sulfonate, viscosity, zeta potential
Background: The electroarthrogram (EAG) is a recording of electrical potentials near a joint, usually a knee, that are generated when the articular cartilage is rhythmically compressed by weight bearing. Physics-based, quantitative prediction of the magnitude of the electroarthrogram remains an open problem. Methods and Results: A new electromechanical model is created to describe viscous displacement of sodium ions from fixed sulfonate anions in the cartilage matrix by radial flow of interstitial fluid during side-to-side shifting of body weight. The viscous force exerted by fluid flow slightly displaces the Na+ ion in its potential energy well. The ion pairs form electrical dipoles, for which changes in measured electrical potential fields are calculated and integrated. The EAG signal from any small volume in a disk of cartilage is computable directly from a lumped physical constant, the radial distance from the center of the disk, the compressive strain rate, the local thickness of the cartilage, as well as other geometric and physical factors. There are no unknown free parameters. There are no arbitrary constants. The predicted amplitude of the EAG voltage for a normal human subject is 0.174 mV, compared to 0.035 mV for a person with moderate to severe osteoarthritis. These values are within the ranges of measured EAG amplitudes in humans. Conclusions: The new model provides a first-principles, physically plausible explanation of streaming potentials in cartilage. The algebraic form of the equation predicting EAG amplitude offers insights into the key variables that determine the EAG signal and could lead to more informed and useful applications of electroarthrography.