Predictive modeling of drug effects on electrocardiograms

Whole electrocardiogram (ECG) waveform analysis is a technique for evaluating aggregate arrhythmic risks of drugs. In this paper, we propose methods for exploring changes to ECG morphology due to drug effects using Gaussian model parameters, and predict patient specific post-drug ECG based on pre-drug ECG. We evaluate the proposed methods using clinical ECG recordings from subjects under the effect of anti-arrhythmic drugs Dofetilide, Quinidine, Ranolazine, and Verapamil, from the ECGRVDQ database on PhysioNet. Paired-sample t-test p-values (>0.05) suggest the proposed method can achieve similar results when compared to expert annotated J to T_peak and T_peak to T_end intervals for all four drug states. We employed a leave-one-out cross validation strategy to train the prediction model and produce the results. Mean Pearson correlations between all predicted and recorded post-drug waveform morphologies for all drug states across both the vector magnitude lead and Lead II is 0.94±0.05, with p-values <0.01 for all predictions; indicating significant predictions. Parameters from ECG models with Gaussian basis can be used to calculate clinically useful information and to capture or predict changes in cardiac signals due to drug effects.