{
  "id": "1812.10386",
  "version": "v1",
  "published": "2018-12-26T17:08:54.000Z",
  "updated": "2018-12-26T17:08:54.000Z",
  "title": "ECG Segmentation by Neural Networks: Errors and Correction",
  "authors": [
    "Iana Sereda",
    "Sergey Alekseev",
    "Aleksandra Koneva",
    "Roman Kataev",
    "Grigory Osipov"
  ],
  "categories": [
    "cs.LG",
    "eess.SP",
    "stat.ML"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-12-26T17:08:54.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural networks",
      "ecg segmentation",
      "error correction occurs",
      "deep convolutional networks",
      "important applied problem"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}