Deep Learning for ECG Classification

2017-01-01 · Journal of Physics: Conference Series 2017 1 · Boris Pyakillya, Natasha Kazachenko, Nick Mikhailovsky

The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

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Code (2)

ismorphism/DeepECG tf

karaposu/DeepECG tf

Tasks

ClassificationDeep LearningECG ClassificationElectrocardiography (ECG)General Classification

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