Classification of 12-Lead ECG Signals with Bi-directional LSTM Network

Ahmed Mostayed; Junye Luo; Xingliang Shu; William Wee

Classification of 12-Lead ECG Signals with Bi-directional LSTM Network

Computer Vision and Pattern Recognition 2018-11-07 v1

Authors: Ahmed Mostayed , Junye Luo , Xingliang Shu , William Wee

Abstract

We propose a recurrent neural network classifier to detect pathologies in 12-lead ECG signals and train and validate the classifier with the Chinese physiological signal challenge dataset (http://www.icbeb.org/Challenge.html). The recurrent neural network consists of two bi-directional LSTM layers and can train on arbitrary-length ECG signals. Our best trained model achieved an average F1 score of 74.15% on the validation set. Keywords: ECG classification, Deep learning, RNN, Bi-directional LSTM, QRS detection.

Keywords

ecg classification deep learning recurrent neural network bi-directional lstm 12-lead ecg qrs detection f1 score

Cite

@article{arxiv.1811.02090,
  title  = {Classification of 12-Lead ECG Signals with Bi-directional LSTM Network},
  author = {Ahmed Mostayed and Junye Luo and Xingliang Shu and William Wee},
  journal= {arXiv preprint arXiv:1811.02090},
  year   = {2018}
}

Related papers

View all related →