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

Abstract

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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$F_{1}$ \end{document} 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.

Cardiac abnormalities from 12‐Lead ECG signals prediction based on deep convolutional neural network optimized with nomadic people optimization algorithm

“…For some attributes, missing value imputation is used to greater value of the provided attribute. This section explains transformation using OHE with additional attribute is described in Equation (7).…”

“…Better ECG interpretation algorithms are certainly needed. [5][6][7][8] In the field of machine learning, there has been a significant advancement in recent years. 9,10 Deep Learning is a subfield of machine learning in which increasingly complicated neural network topologies can examine with the count of data depends on the performance.…”

“…The procedure of interpreting an ECG is time‐consuming and tiresome, and it is prone to mistakes. Better ECG interpretation algorithms are certainly needed 5–8 . In the field of machine learning, there has been a significant advancement in recent years 9,10 .…”

Optimal AdaBoost kernel support vector machine for monitoring arrhythmia patients utilizing Internet of Things‐cloud environment

“…From the analysis, the accuracy rate was high than other techniques. The multi‐lead‐branch fusion network (MLBF‐Net) based 12‐lead ECG for arrhythmia classification was elaborated by Zhang et al 18 The MLBF‐Net has three elements like cross‐lead features fusion, multi‐loss co‐optimization, and multiple lead‐specific branches. The parameters such as precision, recall, and F1‐score were used for identifying the types of arrhythmias.…”

A Parallel Cross Convolutional Recurrent Neural Network for Automatic Imbalanced ECG Arrhythmia Detection with Continuous Wavelet Transform

“…The ECG is used to record the electrical activities and states of the heart over time through electrodes attached to the skin surface [ 3 ]. Thus, the presence of abnormal heart electrical activity can be detected by analyzing the ECG, which can assist to derive the type of arrhythmia in turn.…”