  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 $F_1$ 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.
