{
  "id": "1904.00138",
  "version": "v1",
  "published": "2019-03-30T02:50:23.000Z",
  "updated": "2019-03-30T02:50:23.000Z",
  "title": "On Arrhythmia Detection by Deep Learning and Multidimensional Representation",
  "authors": [
    "K. S. Rajput",
    "S. Wibowo",
    "C. Hao",
    "M. Majmudar"
  ],
  "comment": "draft paper; prepared for journal",
  "categories": [
    "stat.ML",
    "cs.LG",
    "eess.SP"
  ],
  "abstract": "ECG is a time-series signal that is represented by 1-D data. Higher dimensional representation contains more information that is accessible for feature extraction. Hidden variables such as frequency relation and morphology of segment is not directly accessible in the time domain. In this paper, 1-D time series data is converted into multi-dimensional representation in the form of multichannel 2-D images. Following that, deep learning was used to train a deep neural network based classifier to detect arrhythmias. The validation is performed by comparing the output of deep learning with the annotation that was annotated by committees that consist of several certified cardiologists.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-03-30T02:50:23.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep learning",
      "multidimensional representation",
      "arrhythmia detection",
      "higher dimensional representation contains",
      "deep neural network"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}