ECG Segmentation by Neural Networks: Errors and Correction
This work addresses ECG segmentation, a domain-specific medical application, with incremental improvements in error analysis.
The study investigated error correction in an ensemble of deep convolutional networks for ECG segmentation and explored using ensemble errors to assess data representation quality, demonstrating an outlier distillation effect.
In this study we examined the question of how error correction occurs in an ensemble of deep convolutional networks, trained for an important applied problem: segmentation of Electrocardiograms(ECG). We also explore the possibility of using the information about ensemble errors to evaluate a quality of data representation, built by the network. This possibility arises from the effect of distillation of outliers, which was demonstarted for the ensemble, described in this paper.