Model-Based Feature Augmentation for Cardiac Ablation Target Learning From Images

IEEE Trans Biomed Eng. 2019 Jan;66(1):30-40. doi: 10.1109/TBME.2018.2818300. Epub 2018 Mar 22.


Goal: We present a model-based feature augmentation scheme to improve the performance of a learning algorithm for the detection of cardiac radio-frequency ablation (RFA) targets with respect to learning from images alone.

Methods: Initially, we compute image features from delayed-enhanced magnetic resonance imaging (DE-MRI) to describe local tissue heterogeneities and feed them into a machine learning framework with uncertainty assessment for the identification of potential ablation targets. Next, we introduce the use of a patient-specific image-based model derived from DE-MRI coupled with the Mitchell-Schaeffer electrophysiology model and a dipole formulation for the simulation of intracardiac electrograms. Relevant features are extracted from these simulated signals which serve as a feature augmentation scheme for the learning algorithm. We assess the classifier's performance when using only image features and with model-based feature augmentation.

Results: We obtained average classification scores of 97.2 % accuracy, 82.4 % sensitivity, and 95.0 % positive predictive value by using a model-based feature augmentation scheme. Preliminary results also show that training the algorithm on the closest patient from the database, instead of using all the patients, improves the classification results.

Conclusion: We presented a feature augmentation scheme based on biophysical cardiac electrophysiology modeling to increase the prediction scores of a machine learning framework for the RFA target prediction.

Significance: The results derived from this study are a proof of concept that the use of model-based feature augmentation strengthens the performance of a purely image driven learning scheme for the prediction of cardiac ablation targets.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Catheter Ablation / methods*
  • Heart / diagnostic imaging
  • Heart / physiopathology
  • Humans
  • Image Interpretation, Computer-Assisted / methods
  • Machine Learning*
  • Magnetic Resonance Imaging / methods*
  • Models, Cardiovascular*
  • Sensitivity and Specificity