  Electrocardiogram (ECG) signals, profiling the electrical activities of the
heart, are used for a plethora of diagnostic applications. However, ECG systems
require multiple leads or channels of signals to capture the complete view of
the cardiac system, which limits their application in smartwatches and
wearables. In this work, we propose a modally reduced representation learning
method for ECG signals that is capable of generating channel-agnostic, unified
representations for ECG signals. Through joint optimization of reconstruction
and alignment, we ensure that the embeddings of the different channels contain
an amalgamation of the overall information across channels while also retaining
their specific information. On an independent test dataset, we generated highly
correlated channel embeddings from different ECG channels, leading to a
moderate approximation of the 12-lead signals from a single-channel embedding.
Our generated embeddings can work as competent features for ECG signals for
downstream tasks.
