  With rise of interventional cardiology, Catheter Ablation Therapy (CAT) has
established itself as a first-line solution to treat cardiac arrhythmia.
Although CAT is a promising technique, cardiologist lacks vision inside the
body during the procedure, which may cause serious clinical syndromes. To
support accurate clinical procedure, Contact Force Sensing (CFS) system is
developed to find a position of the catheter tip through the measure of contact
force between catheter and heart tissue. However, the practical usability of
commercialized CFS systems is not fully understood due to inaccuracy in the
measurement. To support the development of more accurate system, we develop a
full pipeline of CFS system with newly collected benchmark dataset through a
contact force sensing catheter in simplest hardware form. Our dataset was
roughly collected with human noise to increase data diversity. Through the
analysis of the dataset, we identify a problem defined as Shift of Reference
(SoR), which prevents accurate measurement of contact force. To overcome the
problem, we conduct the contact force estimation via standard deep neural
networks including for Recurrent Neural Network (RNN), Fully Convolutional
Network (FCN) and Transformer. An average error in measurement for RNN, FCN and
Transformer are, respectively, 2.46g, 3.03g and 3.01g. Through these studies,
we try to lay a groundwork, serve a performance criteria for future CFS system
research and open a publicly available dataset to public.
