  We propose the Nonlinear Regression Convolutional Encoder-Decoder (NRCED), a
novel framework for mapping a multivariate input to a multivariate output. In
particular, we implement our algorithm within the scope of 12-lead surface
electrocardiogram (ECG) reconstruction from intracardiac electrograms (EGM) and
vice versa. The goal of performing this task is to allow for improved
point-of-care monitoring of patients with an implanted device to treat cardiac
pathologies. We will achieve this goal with 12-lead ECG reconstruction and by
providing a new diagnostic tool for classifying atypical heartbeats. The
algorithm is evaluated on a dataset retroactively collected from 14 patients.
Correlation coefficients calculated between the reconstructed and the actual
ECG show that the proposed NRCED method represents an efficient, accurate, and
superior way to synthesize a 12-lead ECG. We can also achieve the same
reconstruction accuracy with only one EGM lead as input. We also tested the
model in a non-patient specific way and saw a reasonable correlation
coefficient. The model was also executed in the reverse direction to produce
EGM signals from a 12-lead ECG and found that the correlation was comparable to
the forward direction. Lastly, we analyzed the features learned in the model
and determined that the model learns an overcomplete basis of our 12-lead ECG
space. We then use this basis of features to create a new diagnostic tool for
identifying atypical and diseased heartbeats. This resulted in a ROC curve with
an associated area under the curve value of 0.98, demonstrating excellent
discrimination between the two classes.
