Atrial Fibrillation Classification Using Deep Convolution Networks
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We propose the usage of three deep convolutional neural networks architectures for classification of a single lead electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AFIB) classification, for which data set was provided by the Department of Biomedical Engineering, BUT. The compared networks are based on ResNet, VGG net and AlexNet. Single lead signals are transformed into the form of spectrogram. AFIB data was augmented for the purpose of similar size of both respected classes and for successful classification. The most successful architecture, based on AlexNet, was found to perform obtaining an accuracy of 92 % and F1 score of 56 % on the hidden testing set.
KeywordsECG, atrial fibrillation, signal processing classification, deep learning, neural networks, convolution, resnet, alexnet, vgg
Document typePeer reviewed
Document versionFinal PDF
SourceProceedings I of the 26st Conference STUDENT EEICT 2020: General papers. s. 345-349. ISBN 978-80-214-5867-3
- Student EEICT 2020