Dec 9, 2018 · In this paper, we introduce a two-channel deep neural network to more accurately detect AF presented in the ECG signal.

The experimental results confirm that the proposed model significantly improves the performance of AF detection on well-known MIT-BIH AF database with 5-s ECG ...

In this paper, we introduce a two-channel deep neural network to more accurately detect the presence of AF in the ECG signals.

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 ...

The model architecture is a two-channel deep neural network. The top channel takes the row windowed signal as input and includes an attention strategy to ...

A two-channel deep neural network is introduced to more accurately detect the presence of AF in the ECG signals and can guide the physicians via ...

Mar 13, 2019 · The model shows via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. In ...

1. ECGNET. 99.40%. ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention ; 2. Wavelet transform + 2D CNN. 99.16%.

ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention. no code yet • arXiv:1812.07422 2018. The complexity of the ...

In this context, we propose an artificial neural network ANN application to classify ECG signals into three classes, the first presents Normal Sinus Rhythm NSR, ...