Classification of drug‐induced hERG potassium‐channel block from electrocardiographic T‐wave features using artificial neural networks

Background Human ether‐à‐go‐go‐related gene ( hERG ) potassium‐channel block represents a harmful side effect of drug therapy that may cause torsade de pointes (TdP). Analysis of ventricular repolarization through electrocardiographic T‐wave features represents a noninvasive way to accurately evaluate the TdP risk in drug‐safety studies. This study proposes an artificial neural network ( ANN ) for noninvasive electrocardiography‐based classification of the hERG potassium‐channel block. Methods The data were taken from the “ ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects” Physionet database; they consisted of median vector magnitude ( VM ) beats of 22 healthy subjects receiving a single 500 μg dose of dofetilide. Fourteen VM beats were considered for each subject, relative to time‐points ranging from 0.5 hr before to 14.0 hr after dofetilide administration. For each VM , changes in two indexes accounting for the early and the late phases of repolarization, Δ ERD 30% and Δ T S /A , respectively, were computed as difference between values at each postdose time‐point and the predose time‐point. Thus, the dataset contained 286 Δ ERD 30% ‐Δ T S /A pairs, partitioned into training, validation, and test sets (114, 29, and 143 pairs, respectively) and used as inputs of a two‐layer feedforward ANN with two target classes: high block ( HB ) and low block ( LB ). Optimal ANN ( OANN ) was identified using the training and validation sets and tested on the test set. Results Test set area under the receiver operating characteristic was 0.91; sensitivity, specificity, accuracy, and precision were 0.93, 0.83, 0.92, and 0.96, respectively. Conclusion OANN represents a reliable tool for noninvasive assessment of the hERG potassium‐channel block.