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2018 | OriginalPaper | Chapter

Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks

Authors : Jungrae Cho, Yoonnyun Kim, Minho Lee

Published in: PRedictive Intelligence in MEdicine

Publisher: Springer International Publishing

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Abstract

Recently, many researchers have attempted to apply deep neural networks to detect Atrial Fibrillation (AF). In this paper, we propose an approach for prediction of AF instead of detection using Deep Convolutional Neural Networks (DCNN). This is done by classifying electrocardiogram (ECG) before AF into normal and abnormal states, which is hard for the cardiologists to distinguish from the normal sinus rhythm. ECG is transformed into spectrogram and trained using VGG16 networks to predict normal and abnormal signals. By changing the time length of abnormal signals and making up their own datasets for preprocessing, we investigate the changes in F1-score for each dataset to explore the right time to alert the occurrence of AF.

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previous chapter Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks
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Metadata
Title
Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks
Authors
Jungrae Cho
Yoonnyun Kim
Minho Lee
Copyright Year
2018
Book
PRedictive Intelligence in MEdicine

Print ISBN: 978-3-030-00319-7

Electronic ISBN: 978-3-030-00320-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00320-3_20

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