# Classification of 12-Lead ECG Signals with Bi-directional LSTM Network

arXiv: https://arxiv.org/abs/1811.02090 · HTML: https://mlsanity.com/paper/1811.02090/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement](https://mlsanity.com/paper/2301.10171/) (2023)
- [MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classification](https://mlsanity.com/paper/2306.12098/) (2023)
- [MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Diagnosis](https://mlsanity.com/paper/2311.04224/) (2023)
- [Comparative Analysis of Data Augmentation for Clinical ECG Classification with STAR](https://mlsanity.com/paper/2510.24740/) (2025)
- [CRT-Net: A Generalized and Scalable Framework for the Computer-Aided Diagnosis of Electrocardiogram Signals](https://mlsanity.com/paper/2105.13619/) (2021)
- [SIM-ECG: A Signal Importance Mask-driven ECGClassification System](https://mlsanity.com/paper/2110.14835/) (2021)
- [A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance](https://mlsanity.com/paper/2208.00323/) (2022)
- [Revisiting the Disequilibrium Issues in Tackling Heart Disease Classification Tasks](https://mlsanity.com/paper/2407.20249/) (2024)

## Abstract (arXiv)

We propose a recurrent neural network classifier to detect pathologies in 12-lead ECG signals and train and validate the classifier with the Chinese physiological signal challenge dataset (http://www.icbeb.org/Challenge.html). The recurrent neural network consists of two bi-directional LSTM layers and can train on arbitrary-length ECG signals. Our best trained model achieved an average F1 score of 74.15% on the validation set. Keywords: ECG classification, Deep learning, RNN, Bi-directional LSTM, QRS detection.