Jun 2, 2020

MultiFusionNet: Atrial Fibrillation Detection With Deep Neural Networks

AMIA Joint Summits on Translational Science Proceedings
Luan TranLi Xiong

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia as well as a significant risk factor in heart failure and coronary artery disease. AF can be detected by using a short ECG recording. However, discriminating atrial fibrillation from normal sinus rhythm, other arrhythmia and strong noise, given a short ECG recording, is challenging. Towards this end, we propose MultiFusionNet, a deep learning network that uses a multiplicative fusion method to combine two deep neural networks trained on different sources of knowledge, i.e., extracted features and raw data. Thus, MultiFusionNet can exploit the relevant extracted features to improve upon the utilization of the deep learning model on the raw data. Our experiments show that this approach offers the most accurate AF classification and outperforms recently published algorithms that either use extracted features or raw data separately. Finally, we show that our multiplicative fusion method for combining the two sub-networks outperforms several other combining methods.

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Mentioned in this Paper

Sinus Rhythm
Electrocardiogram
Heart Failure
Detected (Finding)
Coronary Arteriosclerosis
Learning
Respiratory Sinus Arrhythmia
Detection
Other Cardiac Dysrhythmias
Neural Networks (Anatomic)

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