Key result
CNN-SVM algorithm predicts atrial fibrillation recurrence with ~96% accuracy using preoperative surface mapping.
Why the study?
Despite radio frequency ablation being the most effective treatment for AF, recurrence rates remain high, prompting the need to predict AF recurrence preoperatively using BSPM signals to improve treatment planning.
Does a CNN-SVM algorithm improve the prediction of atrial fibrillation recurrence in patients undergoing ablation compared to CNN-MLP?
Does a CNN-SVM algorithm improve the prediction of atrial fibrillation recurrence in patients undergoing ablation compared to CNN-MLP?
Answers are grounded in this paper and cite their sources.
A novel CNN-SVM algorithm using preoperative BSPM signals demonstrates high accuracy in predicting AF recurrence after ablation, potentially aiding in personalized treatment planning.
Although radio frequency ablation is the most effective treatment for atrial fibrillation (AF), there is still a high recurrence rate. The purpose of this paper was to initially assess the probability of the recurrence of AF based on the preoperative body surface potential mapping (BSPM) signals, in other words, to predict the efficiency of ablation and assist physicians in developing more effective treatment options. At present, deep learning methods based on convolutional neural networks (CNNs) do not require complex mathematical abstractions or manual interventions; thus, higher computation efficiency can be obtained in such research. However, the use of the fully connected multi-layer perceptron (MLP) algorithms has shown low classification performance. This paper proposes an improved CNN algorithm (CNN-SVM method) for the recurrence classification in AF patients by combining with the support vector machine (SVM) architecture. The algorithm is validated on the preoperative AF signals of 14 patients for classification. All postoperative patients are followed up for one year; ten of them remain in sinus rhythm, whereas the other four turn back to AF. The ECG data for these patients are obtained through the 128-Lead BSPM system. The results show that the proposed CNN-SVM method can automatically extract the characteristic information through the CNN network. The constructed model ultimately achieved an accuracy of 96%, a sensitivity of 88%, and a specificity of 96%. It is concluded that the CNN-SVM method solves the drawbacks of MLP only for separating linear data. It improves the overall performance of AF recurrence classification, thereby providing a valuable reference for doctors to develop personalized treatment plans.
No takes yet. Share an insight, caveat, or question.
Li et al. (2019) studied Atrial fibrillation (n=14). CNN-SVM algorithm based on preoperative BSPM signals vs. CNN with MLP algorithm was evaluated on Classification accuracy for AF recurrence. The proposed CNN-SVM algorithm achieved an accuracy of 96%, sensitivity of 88%, and specificity of 96% for predicting atrial fibrillation recurrence.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: