Efficient solutions of cardiac membrane models using novel unsupervised clustering algorithm.
We present a method to efficiently solve cardiac membrane models using a novel unsupervised clustering algorithm. The unsupervised clustering algorithm was designed to handle repeated clustering of multidimensional objects with rapidly changing properties. A Modified Trie datastructure that allowed efficient search, scalable and distributed assembly of the result was designed. The method was applied to solve monodomain models of cardiac tissue with highly non-linear reaction elements. We demonstrate the versatility and advantages of using the method by subjecting the tissue to various spatial excitation patterns.
Hussan
Jagir R
JR
Auckland Bioengineering Institute, University of Auckland, New Zealand. r.jagir@auckland.ac.nz
Trew
Mark L
ML
Hunter
Peter J
PJ
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001185:Artificial Intelligence; D002462:Cell Membrane / Q000502:physiology*; D016000:Cluster Analysis; D003198:Computer Simulation; D006329:Heart Conduction System / Q000502:physiology*; D006801:Humans; D008564:Membrane Potentials / Q000502:physiology*; D008955:Models, Cardiovascular