Poster Abstract: Automated Detection of the Onset of Ventricular Depolarization in Challenging Clinical ECG Data

This paper presents a novel method for automatically detecting the onset of ventricular depolarization in electrocardiogram (ECG). In order to accommodate highly variable ECG morphologies in potentially noisy ECG signals, a weighted combination of factors that are consistent with the onset of ventricular depolarization is computed. Weight parameters are optimized to maximize the detection accuracy. The proposed method is evaluated against diverse datasets, yielding a bias of 1.69 ms, standard deviation of 10.55 ms, and mean absolute error of 6.68 ms.