[
  {
    "Accuracy": 95.9,
    "code_links": [
      {
        "title": "dave-fernandes/ECGClassifier",
        "url": "https://github.com/dave-fernandes/ECGClassifier"
      },
      {
        "title": "ljleeworking/4-Heartbeat-Categorization-from-ECG-Signal",
        "url": "https://github.com/ljleeworking/4-Heartbeat-Categorization-from-ECG-Signal"
      }
    ],
    "date": "2018-04-19",
    "date2": 20180419,
    "model": "Deep residual CNN",
    "paper": {
      "title": "ECG Heartbeat Classification: A Deep Transferable Representation",
      "url": "https://cknow.io/lib/7c444f9d448f85ed"
    },
    "paper_data_uoa": "7c444f9d448f85ed"
  },
  {
    "Accuracy": 94.7,
    "code_links": [],
    "date": "2014-08-01",
    "date2": 20140801,
    "model": "T-wave + Total Integral",
    "paper": {
      "title": "A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal",
      "url": "https://cknow.io/lib/47818e0a930ad05d"
    },
    "paper_data_uoa": "47818e0a930ad05d"
  },
  {
    "Accuracy": 93.5,
    "code_links": [],
    "date": "2017-06-01",
    "date2": 20170601,
    "model": "CNN",
    "paper": {
      "title": "Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals",
      "url": "https://cknow.io/lib/b5d184521aad2ebd"
    },
    "paper_data_uoa": "b5d184521aad2ebd"
  }
]