Neural Network-Based Matrix Completion for Minimal Configuration of Body Surface Potential Mapping

Body surface potential mapping (BSPM) provides high spatial resolution recordings of the electric potential of the heart on the body surface. BSPM can involve up to 200 electrodes, in contrast to standard 12-lead ECG. The costs and complexity of a BSPM procedure are a limiting factor to its use in clinical practice. Both can be reduced by using fewer electrodes and reconstructing signals from the missing electrodes with an artificial neural network. The minimal configuration consists of the electrodes that are most relevant for reliable reconstruction. We propose an architecture for a variational autoencoder, trained on BSPM procedures from the Nijmegen dataset: EDGAR [1], to reconstruct a full 65-lead system from a reduced number of input electrodes. Further, we determine the effect of an increased numbers of missing electrodes on the corresponding reconstruction error, and show that it is possible to achieve a good 65-lead reconstruction from as few as 12 electrodes. We consider the implication of our research in the scope of current BSPM practice, as well as the limitations of using neural networks for this task.