Problems of representation of electrocardiograms in convolutional neural networks

Iana Sereda, Sergey Alekseev, Aleksandra Koneva, Alexey Khorkin, Grigory Osipov

Using electrocardiograms as an example, we demonstrate the characteristic problems that arise when modeling one-dimensional signals containing inaccurate repeating pattern by means of standard convolutional networks. We show that these problems are systemic in nature. They are due to how convolutional networks work with composite objects, parts of which are not fixed rigidly, but have significant mobility. We also demonstrate some counterintuitive effects related to generalization in deep networks.

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