Key result
A new graph-based algorithm successfully customized patient-specific Purkinje system models using measured ECGs, achieving a remaining root mean square error of 4.05 mV.
Population
Computational model of patient-specific Purkinje activation based on measured ECGs
Authors
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May support ECG-driven Purkinje personalization; leaves open clinical translation pending prospective validation.
A novel graph-based algorithm allows for computationally efficient, patient-specific modeling of the Purkinje system based on measured surface ECGs.
Kahlmann et al. (2017) studied Cardiac electrophysiology modeling. Patient-specific Purkinje activation modeling based on measured ECGs was evaluated on Root mean square error (RMSE) between simulated and measured QRS complexes. A new graph-based algorithm successfully customized patient-specific Purkinje system models using measured ECGs, achieving a remaining root mean square error of 4.05 mV.
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