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arXiv 1811.020902018-11-05cs.CV

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

University of Cincinnati（辛辛那提大学）

Ahmed Mostayed, Junye Luo, Xingliang Shu, William Wee

英文摘要：

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.

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