# Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM

arXiv: https://arxiv.org/abs/2011.06187 · HTML: https://mlsanity.com/paper/2011.06187/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network](https://mlsanity.com/paper/2202.05177/) (2022)
- [A CNN-based Local-Global Self-Attention via Averaged Window Embeddings for Hierarchical ECG Analysis](https://mlsanity.com/paper/2504.16097/) (2025)

## Abstract (arXiv)

It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals. Implementing an automated ECG signal detection system can help diagnosis arrhythmia in order to improve the accuracy of diagnosis. In this paper, we proposed, implemented, and compared an automated system using two different frameworks of the combination of convolutional neural network (CNN) and long-short term memory (LSTM) for classifying normal sinus signals, atrial fibrillation, and other noisy signals. The dataset we used is from the MIT-BIT Arrhythmia Physionet. Our approach demonstrated that the cascade of two deep learning network has higher performance than the concatenation of them, achieving a weighted f1 score of 0.82. The experimental results have successfully validated that the cascade of CNN and LSTM can achieve satisfactory performance on discriminating ECG signals.