  Diagnosis of cardiovascular diseases usually relies on the widely used
standard 12-Lead (S12) ECG system. However, such a system could be bulky, too
resource-intensive, and too specialized for personalized home-based monitoring.
In contrast, clinicians are generally not trained on the alternative proposal,
i.e., the reduced lead (RL) system. This necessitates mapping RL to S12. In
this context, to improve upon traditional linear transformation (LT)
techniques, artificial intelligence (AI) approaches like long short-term memory
(LSTM) networks capturing non-linear temporal dependencies, have been
suggested. However, LSTM does not adequately interpolate spatially (in 3D). To
fill this gap, we propose a combined LSTM-UNet model that also handles spatial
aspects of the problem, and demonstrate performance improvement. Evaluated on
PhysioNet PTBDB database, our LSTM-UNet achieved a mean R^2 value of 94.37%,
surpassing LSTM by 0.79% and LT by 2.73%. Similarly, for PhysioNet INCARTDB
database, LSTM-UNet achieved a mean R^2 value of 93.91%, outperforming LSTM by
1.78% and LT by 12.17%.
