ECG Segmentation by Neural Networks: Errors and Correction

Iana Sereda, Sergey Alekseev, Aleksandra Koneva, Roman Kataev, Grigory Osipov

Published 2018-12-26Version 1

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.