A bi-directional LSTM recurrent neural network can classify 12-lead ECG signals into 9 categories with an average F1 score of 74.15%, demonstrating the potential of deep learning for automated arrhythmia detection.
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Performance on internal validation warrants external testing; leaves open routine clinical deployment of automated ECG classification.
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.
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Mostayed et al. (2018) studied this question.
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