Higher-dimensional embedding of time-series data for machine learning.

Introduction: Deep learning has revolutionized image analysis, yet most clinical biosignals, especially multi-lead electrocardiograms (ECGs), remain one-dimensional and awkward for modern vision models. Methods: We introduce an orthogonal-polynomial imaging (OPI) framework that encodes 12-lead ECGs into a single two-dimensional image through an invertible transformation prior to coarse-graining, producing compact image representations that retain sufficient information for accurate classification. We benchmark the proposed representation against established architectures including ResNet, Transformers, and 1D temporal convolutional models on large-scale PhysioNet ECG datasets. Results: Our results demonstrate that OPI-based image representations achieve highly competitive classification performance while preserving diagnostically relevant temporal and cross-lead information. Discussion: The framework bridges raw multi-channel biosignals and standard image-based learning architectures without representation loss prior to coarse-graining, offering an effective, scalable, and physics-inspired representation for automated cardiac diagnostics.