POSTER: Atrial Fibrillation Detection Using a Double-Layer Bi-Directional LSTM Neural Networks

Alsaleem M., Islam M. S.

1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020, Riyadh, Saudi Arabia, 3 - 05 November 2020, pp.266-267 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/smart-tech49988.2020.00071
  • City: Riyadh
  • Country: Saudi Arabia
  • Page Numbers: pp.266-267
  • Keywords: atrial fibrillation (AF), Deep learning(DL), electrocardiogram (ECG), long short term memory (LSTM)
  • TED University Affiliated: No

Abstract

Atrial fibrillation (AF) is the most common heart disorder manifested as an abnormal rhythm of irregular heartbeats that could lead to strokes and death. In this paper, we propose a double-layer bi-directional long short term memory (LSTM) neural network to classify a short segment of ECG signal transformed into spectrogram. We also use a preprocessing step to augment the dataset to achieve better classification performance. We conducted different experiments on different segment lengths and different network parameters using PhysioNet Challenge 2017 dataset and we achieved a total accuracy of 91.4% of classifying AF signals outperforming existing methods.