Key result
A support vector machine model trained on local activation time features classified simulated cardiac excitation patterns during atrial fibrillation with an overall accuracy of 98.4%.
A machine learning approach using support vector machines can accurately classify simulated cardiac excitation patterns during atrial fibrillation based on local activation times.
Hypothesis-generating for ML-based AF mapping in simulation; requires prospective clinical validation before any use.
The goal of this research was to classify cardiac excitation patterns during atrial fibrillation (AFib). For this purpose, virtual models of intracardiac mapping catheters were moved across in-silico cardiac tissue to extract local activation times (LATs) of each catheter electrode from simulated cardiac action potential (AP) signals. The resulting LAT patterns consisting of the LATs of all electrodes resemble patterns measured in clinical cases. The LATs represent the input information for features that were used to separate four different excitation patterns during AFib. Those four excitation patterns were plane wave, ectopic focus (spherical wave), rotor (spiral wave) and block. A feature selection algorithm was used to investigate the features concerning their power to classify the different simulated excitation patterns. The scores of the selected features were used to train and optimize a support vector machine (SVM). The optimized and cross-validated SVM was then used to classify the simulated cardiac excitation patterns. The achieved overall classification accuracy of this SVM model was 98.4 %.
No takes yet. Share an insight, caveat, or question.
Reich et al. (2016) studied Atrial fibrillation. Support vector machine (SVM) classification was evaluated on Overall classification accuracy of four excitation patterns. A support vector machine model trained on local activation time features classified simulated cardiac excitation patterns during atrial fibrillation with an overall accuracy of 98.4%.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: