# MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG

arXiv: https://arxiv.org/abs/2008.07263 · HTML: https://mlsanity.com/paper/2008.07263/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

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

- [ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text](https://mlsanity.com/paper/2405.19366/) (2024)
- [Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement](https://mlsanity.com/paper/2301.10171/) (2023)
- [Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification](https://mlsanity.com/paper/2305.18592/) (2023)
- [IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis](https://mlsanity.com/paper/2312.09445/) (2023)
- [ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning](https://mlsanity.com/paper/2304.04646/) (2023)
- [LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning](https://mlsanity.com/paper/2210.07340/) (2022)
- [Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review](https://mlsanity.com/paper/2308.13714/) (2023)
- [FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data](https://mlsanity.com/paper/2411.07050/) (2024)

## Abstract (arXiv)

Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average F1 score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.