Automatic ECG diagnosis model based on bidirectional selective state space model
10.3969/j.issn.1005-202X.2025.04.010
- VernacularTitle:基于双向选择性状态空间模型的心电自动诊断模型
- Author:
Mingjun LIN
1
;
Yaoqi WEN
;
Xin ZHANG
;
Yong HONG
;
Chaomin CHEN
;
Yuliang WU
Author Information
1. 南方医科大学生物医学工程学院,广东 广州 510515
- Publication Type:Journal Article
- Keywords:
electrocardiogram;
automatic electrocardiogram diagnosis model;
elective state space model;
deep learning
- From:
Chinese Journal of Medical Physics
2025;42(4):489-495
- CountryChina
- Language:Chinese
-
Abstract:
To address the limitations of the existing automatic electrocardiogram(ECG)diagnosis models in learning long-term dependencies,an automatic 12-lead long-term ECG signal diagnosis model which combines bidirectional selective state space model(bidirectional mamba,BiMamba)with residual multi-scale receptive field block(RMSF)is proposed:(1)designing a multi-scale receptive field module with residual connections to realize more extensive feature extraction and fusion;(2)introducing BiMamba block to enhance the model's temporal modeling capability by employing both forward and backward temporal processing;(3)using the classifier to process features from BiMamba for accomplishing multi-label ECG classification.Five major diagnostic categories from the PTB-XL dataset are extracted and subjected to 5-fold cross-validation experiments.The experimental results from the comparative study show that BiMamba-RMSF achieves an average accuracy of 89.42%,an average AUC of 0.9356,and an average F1 score of 72.85%,outperforming the other 4 automatic ECG diagnosis models.Additionally,ablation study further validates the effectiveness of BiMamba block.It is demonstrated that the proposed model has a high precision in the multi-label classification for 12-lead long-term ECG signals.
