Improving the Catheter Ablation Outcomes via Wavelet Scattering Transform Coefficients in Human Persistent Atrial Fibrillation

The success rate of ablation in persistent atrial fibrillation (persAF) is still suboptimal. Machine learning (ML) with the help of wavelet scattering transform (WST) coefficients might enhance the ablation outcomes. 3206 non-contact electrograms (EGMs) were collected before and after ablation procedures using a balloon catheter from 10 patients with persAF. EGM signals were categorized into two classes: 1490 EGMs were labelled as positive ablation responses (AF termination or AF cycle length (AFCL) increased (≥10msec), and 1716 EGMs were labelled as negative responses (AFCL increase (<10msec)) to catheter ablation. WST coefficients were extracted after applying QRST subtraction from each EGM to remove the effect of far-field ventricular activity. These coefficients were fed into dimensionality reduction techniques. The original features (coefficients) and the transferred features were used to train and test 10 machine learning models using leave-one-patient-out 10-fold cross-validation (LOPOCV) method. The best scenario involved using the Morlet wavelet function in WST, PCA, and the decision tree (DT) classifier. The overall accuracy, sensitivity, specificity, precision, F1-score, AUROC, and balanced accuracy ranged between 75% and 82% across the 10-fold CV. We conclude that the WST coefficients, with the help of ML models, played an important role in predicting the responses of ablating the EGMs and their effect on AF termination and CL changes. A comparison was made between the proposed method and our previous work, showing the superiority of this approach.