Key result
Stacking ensemble model using P-wave morphology and HRV predicts AF with 92% accuracy.
Why the study?
Atrial fibrillation is paroxysmal and often asymptomatic between episodes, making early detection on ECG difficult despite the risk of condition worsening and stroke.
Does a Stacking ensemble learning model using P-wave morphology and heart rate variability parameters improve the early prediction of atrial fibrillation?
Does a Stacking ensemble learning model using P-wave morphology and heart rate variability parameters improve the early prediction of atrial fibrillation?
A Stacking ensemble learning model combining P-wave morphology and heart rate variability parameters from ECGs can accurately predict paroxysmal atrial fibrillation.
Answers are grounded in this paper and cite their sources.
May support ECG-based AF prediction tools; leaves open prospective validation before clinical use.
BACKGROUND: Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model. RESULTS: This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911. CONCLUSION: In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model's predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.
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Wu et al. (2021) studied Atrial fibrillation. Stacking ensemble learning method vs. Bagging and AdaBoost ensemble learning methods was evaluated on Prediction of atrial fibrillation (AUC 0.911). A Stacking ensemble learning model using P-wave morphology and heart rate variability parameters predicted atrial fibrillation with 92% accuracy, 88% sensitivity, 96% specificity, and an AUC of 0.911.
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