Research on arrhythmia classification algorithm based on adaptive multi-feature fusion network.
10.7507/1001-5515.202406069
- Author:
Mengmeng HUANG
1
;
Mingfeng JIANG
2
;
Yang LI
2
;
Xiaoyu HE
2
;
Zefeng WANG
3
;
Yongquan WU
3
;
Wei KE
4
Author Information
1. School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, P. R. China.
2. School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou 310018, P. R. China.
3. Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing 100029, P. R. China.
4. Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, P. R. China.
- Publication Type:Journal Article
- Keywords:
Arrhythmia;
Convolutional neural network;
Electrocardiogram classification;
Feature fusion;
Multi-feature
- MeSH:
Humans;
Arrhythmias, Cardiac/diagnosis*;
Algorithms;
Electrocardiography/methods*;
Neural Networks, Computer;
Signal Processing, Computer-Assisted;
Deep Learning;
Classification Algorithms
- From:
Journal of Biomedical Engineering
2025;42(1):49-56
- CountryChina
- Language:Chinese
-
Abstract:
Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F 1-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
