Prediction method of paroxysmal atrial fibrillation based on multimodal feature fusion.
10.7507/1001-5515.202403039
- Author:
Yongjian LI
1
;
Lei LIU
1
;
Meng CHEN
1
;
Yixue LI
1
;
Yuchen WANG
1
;
Shoushui WEI
1
Author Information
1. School of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China.
- Publication Type:Journal Article
- Keywords:
Atrial fibrillation prediction;
Feature selection;
Multi-center data;
Multimodal fusion
- MeSH:
Humans;
Atrial Fibrillation/diagnosis*;
Machine Learning;
Deep Learning;
Risk Assessment/methods*
- From:
Journal of Biomedical Engineering
2025;42(1):42-48
- CountryChina
- Language:Chinese
-
Abstract:
The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.
