Quasi-Orthogonal ECG-Frank XYZ Transformation with Energy-Based Models and Clinical Text

The transformation of 12-Lead electrocardiograms to 3D vectorcardiograms, along with its reverse process, offer numerous advantages for computer visualization, signal transmission and analysis. Recent literature has shown increasing interest in this structured representation, due to its effectiveness in various cardiac evaluations and machine learning-based arrhythmia prediction. Current transformation techniques utilize fixed matrices, often retrieved through regression methods which fail to correlate with patient’s physical characteristics or ongoing diseases. In this paper, we propose the first quasi-orthogonal transformation handling multi-modal input (12-lead ECG and clinical annotations) through a conditional energy-based model. Within our novel probabilistic formulation, the model proposes multiple transformation coefficients without relying on a single fixed approximation to better highlight relationships between latent factors and structured output. The evaluation of our approach, conducted with a nested cross validation on PTB Diagnostic dataset, showcased improved reconstruction precision across various cardiac conditions compared to state-of-the-art techniques (Kors, Dower, and QSLV).