Convolutional recurrent neural networks for electrocardiogram classification


Martin Zihlmann, Dmytro Perekrestenko, and Michael Tschannen


Computing in Cardiology (CinC), Sept. 2017.

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We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017. The first architecture is a deep convolutional neural network (CNN) with averaging-based feature aggregation across time. The second architecture combines convolutional layers for feature extraction with long-short term memory (LSTM) layers for temporal aggregation of features. As a key ingredient of our training procedure we introduce a simple data augmentation scheme for ECG data and demonstrate its effectiveness in the AF classification task at hand. The second architecture was found to outperform the first one, obtaining an F1 score of 82.1% on the hidden challenge testing set.


electrocardiogram, convolutional recurrent neural network, classification, atrial fibrillation


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Copyright Notice: © 2017 M. Zihlmann, D. Perekrestenko, and M. Tschannen.

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