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arXiv · 1710.06122

Convolutional Recurrent Neural Networks for Electrocardiogram Classification

Martin Zihlmann·Dmytro Perekrestenko·Michael Tschannen
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Abstract

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 $F_1$ score of $82.1$% on the hidden challenge testing set.

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Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen. 2017-10-17. Convolutional Recurrent Neural Networks for Electrocardiogram Classification. https://arxiv.org/abs/1710.06122

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