.png){kind=icon}
.png){kind=icon}
Return
Advanced Deep Neural Network with Unified Feature-Aware and Label Embedding for Multi-Label Arrhythmias Classification
.png){kind=icon}
PRE
AI
Z
Y
L
H
L
X
Y
Y
P
X
G
Z
DOI：10.26599/TST.2023.9010162.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease, and it is a challenging task as it requires identifying the label subset most related to each instance. In this paper, by integrating a deep residual neural network and auto-encoder, we propose an advanced deep neural network (DNN) framework with unified feature-aware and label embedding to perform multi-label arrhythmias classification involving 30 types of arrhythmias. Firstly, a deep residual neural network is built to extract the complex pathological features from varying-dimensional electrocardiograms (ECGs). Secondly, the mean square error loss is adopted to learn a latent space associating the deep pathological features and the corresponding label data, and then to achieve unified feature-label embedding. Thirdly, the label-correlation aware loss is introduced to optimize the auto-encoder architecture, which enables our model to exploit label-correlation for improved multi-label prediction. Our proposed DNN model can allow end-to-end training and prediction, which can perform feature-aware, label embedding, and label-correlation aware prediction in a unified framework. Finally, our proposed model is evaluated on the currently largest public dataset worldwide, and achieves the challenge metric scores of 0.492, 0.495, and 0.490 on the 12-lead, 3-lead, and all-lead version ECGs, respectively. The performance of our approach outperforms other current state-of-the-art methods in the leave-one-dataset-out cross-validation setting, which demonstrates that our approach has great competitiveness in identifying a wider range of multi-label arrhythmias.
Keywords:
Training
Measurement
Pathology
Arrhythmia
Mean square error methods
Electrocardiography
Predictive models
Network architecture
Feature extraction
Residual neural networks
electrocardiogram
multi-label arrhythmias classification
deep neural network
label embedding
Journal
T
IF:
3.5
Papers:
987
Citations:
2.5K
Organization
Cited Papers