  The prediction of heart surface potentials using measurements from the body's
surface is known as the inverse problem of electrocardiography. It is an
ill-posed problem due to the multiple factors that affect the heart signal as
it propagates through the body. This report details research performed into a
machine learning solution to signal reconstruction as well as an analysis of
optimal torso electrode positioning for prediction involving different areas of
the heart. The dataset contains simultaneous measurements from a large number
of body surface potential (BSP) and heart surface potential (HSP) electrodes,
as well as their geometric locations, recorded from an experiment using a human
model. Initially, Time Delayed Neural Nets were trained and tested across all
BSP to HSP relationships resulting in a slight trend of increased
reconstruction correlation with decreased separation of electrodes. However,
the TDNNs had overfitted to the data and failed to predict alternate heartbeat
pacings. Feed Forward Neural Nets (FFNNs) were tested in a many BSP to many HSP
prediction method. Again overfitting occurred. To reduce overfitting, the
number of training signals was reduced by analysing the optimal training BSPs
for each HSP when using basic perceptrons. This analysis involved repeat
sampling and ranking of different BSP combinations, initially, using a Monte
Carlo approximation, until being replaced with a meta-heuristic which increased
the yield of successful BSP combinations. Successful reconstructions across
heartbeat pacings were produced using these optimal BSP combinations for 80 of
the 108 HSPs, and future work exists for the testing of this method of
prediction using real patient data.
