# ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention

## TL;DR

This paper introduces a two-channel deep neural network that learns to focus on important ECG segments for atrial fibrillation detection, significantly improving accuracy and interpretability over existing methods.

## Contribution

The paper proposes a novel two-channel deep learning model that automatically learns attention regions in ECG signals for more accurate AF detection.

## Key findings

- Achieved 99.53% sensitivity in AF detection
- Achieved 99.26% specificity in AF detection
- Achieved 99.40% accuracy on MIT-BIH database

## Abstract

The complexity of the patterns associated with Atrial Fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the current signal processing and shallow machine learning approaches to get accurate AF detection results. Deep neural networks have shown to be very powerful to learn the non-linear patterns in the data. While a deep learning approach attempts to learn complex pattern related to the presence of AF in the ECG, they can benefit from knowing which parts of the signal is more important to focus during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect AF presented in the ECG signal. The first channel takes in a preprocessed ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the preprocessed ECG signal to consider all features of entire signals. The model shows via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. In addition, this combination significantly improves the performance of the atrial fibrillation detection (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40% on the MIT-BIH atrial fibrillation database with 5-s ECG segments.)