Fig. 1.
Computational pipeline devised to predict how physiological waveforms under normal sinus rhythm might indicate susceptibility to a future trigger. Our computational pipeline combines population-based cardiac modeling with supervised ML to predict arrhythmia susceptibility. (1) We began by creating a virtual population of myocytes by varying model parameters that correspond to channel conductance and kinetic properties. (2) Next, we applied three individual triggers: IKr Block, ICaL Increase, and Current Injection on the population. (3) This allowed us to create two groups, high- and low-risk cells (cells that were susceptible or resistant to the perturbation, respectively). Susceptible cells were described as myocytes that formed a repolarization failure or EADs. (4) We took features from baseline (pretrigger) state of the cells along with the risk classification labels and fed them to 8-ML classifiers. (5) We evaluated performance of each classifier by computing the area under the receiver operator characteristic curve and kept the results of the superior algorithm. (6) We analyzed the results to define a unique set of features/experiments that can predict an individual's susceptibility to each trigger.