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Updated: Jul 2, 2025
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Intelligent assessment of atrial fibrillation gradation based on sinus rhythm electrocardiogram and baseline
Biqi Tang1, Sen Liu1, Xujian Feng1
1Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China.
Insights
A new model using vectorcardiogram (VCG) and patient data simplifies atrial fibrillation (AF) gradation assessment. This approach offers a computationally efficient and accurate prognostic tool for clinical management.
Area of Science:
- Cardiology
- Medical Informatics
- Biomedical Engineering
Background:
- Atrial fibrillation (AF) is a progressive arrhythmia impacting quality of life.
- Current 4S-AF scheme for AF management is complex and time-consuming, hindering widespread clinical adoption.
- Need for a simplified, objective assessment model for AF gradation in primary care settings.
Purpose of the Study:
- To simplify the evaluation process for AF gradation.
- To develop an objective assessment model for classifying AF severity.
- To leverage physiological signals and machine learning for improved AF management.
Main Methods:
- Retrospective analysis of 189 ECG recordings from 64 patients with AF.
- Annotation of data into mild and severe AF groups based on the 4S-AF scheme.
- Generation of synthesized vectorcardiograms (VCG) from ECGs during sinus rhythm (SR).
- Feature extraction from VCG, ECG, and baseline characteristics (age, sex, medical history).
- Evaluation of machine learning models (SVM, Random Forests, Logistic Regression) with feature selection.
Main Results:
- The Random Forest (RF) model achieved high performance in AF gradation classification.
- An optimized feature set combining VCG and baseline data yielded the best results.
- The RF model demonstrated accuracy (83.02%), sensitivity (80.56%), and specificity (88.24%) in the inter-patient paradigm.
Conclusions:
- Physiological signals, particularly VCG, are valuable for AF gradation evaluation.
- The proposed model effectively distinguishes between mild and severe AF.
- The model's low computational complexity and high performance make it a promising prognostic tool for clinical AF management.
Background:
Atrial fibrillation (AF) is a progressive arrhythmia that significantly affects a patient's quality of life. The 4S-AF scheme is clinically recommended for AF management; however, the evaluation process is complex and time-consuming. This renders its promotion in primary medical institutions challenging. This retrospective study aimed to simplify the evaluation process and present an objective assessment model for AF gradation.
Methods:
In total, 189 12-lead electrocardiogram (ECG) recordings from 64 patients were included in this study. The data were annotated into two groups (mild and severe) according to the 4S-AF scheme. Using a preprocessed ECG during the sinus rhythm (SR), we obtained a synthesized vectorcardiogram (VCG). Subsequently, various features were calculated from both signals, and age, sex, and medical history were included as baseline characteristics. Different machine learning models, including support vector machines, random forests (RF), and logistic regression, were finally tested with a combination of feature selection techniques.
Results:
The proposed method demonstrated excellent performance in the classification of AF gradation. With an optimized feature set of VCG and baseline features, the RF model achieved accuracy, sensitivity, and specificity of 83.02 %, 80.56 %, and 88.24 %, respectively, under the inter-patient paradigm.
Conclusion:
Our results demonstrate the value of physiological signals in AF gradation evaluation, and VCG signals were effective in identifying mild and severe AF. Considering its low computational complexity and high assessment performance, the proposed model is expected to serve as a useful prognostic tool for clinical AF management.
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