Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks


Cho J., Kim Y., Lee M.

1st International Workshop on PRedictive Intelligence in MEdicine (PRIME), Granada, Nicaragua, 16 September 2018, vol.11121, pp.164-171 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 11121
  • Doi Number: 10.1007/978-3-030-00320-3_20
  • City: Granada
  • Country: Nicaragua
  • Page Numbers: pp.164-171

Abstract

Recently, many researchers have attempted to apply deep neural networks to detect Atrial Fibrillation (AF). In this paper, we propose an approach for prediction of AF instead of detection using Deep Convolutional Neural Networks (DCNN). This is done by classifying electrocardiogram (ECG) before AF into normal and abnormal states, which is hard for the cardiologists to distinguish from the normal sinus rhythm. ECG is transformed into spectrogram and trained using VGG16 networks to predict normal and abnormal signals. By changing the time length of abnormal signals and making up their own datasets for preprocessing, we investigate the changes in F1-score for each dataset to explore the right time to alert the occurrence of AF.