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IEEE Trans Biomed Eng ; 62(11): 2685-92, 2015 Nov.
Article in English | MEDLINE | ID: mdl-26080372

ABSTRACT

GOAL: Gastric motility disorders have been associated with abnormal slow wave electrical activity (gastric dysrhythmias). Gastric pacing is a potential therapy for gastric dysrhythmias; however, new pacing protocols are required that can effectively modulate motility patterns, while being power efficient. This study presents a novel comprehensive 3-D multiscale modeling framework of the human stomach, including anisotropic conduction, capable of evaluating pacing strategies. METHODS: A high-resolution anatomically realistic mesh was generated from CT images taken from a human stomach. Principal conduction axes were calculated and embedded within this model based on a modified Laplace-Dirichlet rule-based algorithm. A continuum-based tridomain formulation was implemented and evaluated for performance and used to model the slow-wave propagation, which takes into account the two main cell types present in gastric musculature. Model parameters were found by matching predicted normal slow-wave activity to experimental observation and data. These simulation parameters were applied while modeling an external pacing event to entrain slow-wave patterns. RESULTS: The proposed formulation was found to be two times more efficient than a previous formulation for a normal slow-wave simulation. Convergence analysis showed that a mesh resolution of [Formula: see text] is required for an accurate solution process. CONCLUSION: The effect of different pacing frequencies on entrainment demonstrated that the pacing protocols are limited by the frequency of the native propagation and the refractory period of the cellular activity. SIGNIFICANCE: The model is expected to become an important tool in studying pacing protocols for both efficiency and effectiveness.


Subject(s)
Gastrointestinal Motility/physiology , Imaging, Three-Dimensional/methods , Models, Biological , Stomach/diagnostic imaging , Stomach/physiology , Humans , Tomography, X-Ray Computed
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