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With rise of interventional cardiology, Catheter Ablation Therapy (CAT) hasestablished itself as a first-line solution to treat cardiac arrhythmia.Although CAT is a promising technique, cardiologist lacks vision inside thebody during the procedure, which may cause serious clinical syndromes. Tosupport accurate clinical procedure, Contact Force Sensing (CFS) system isdeveloped to find a position of the catheter tip through the measure of contactforce between catheter and heart tissue. However, the practical usability ofcommercialized CFS systems is not fully understood due to inaccuracy in themeasurement. To support the development of more accurate system, we develop afull pipeline of CFS system with newly collected benchmark dataset through acontact force sensing catheter in simplest hardware form. Our dataset wasroughly collected with human noise to increase data diversity. Through theanalysis of the dataset, we identify a problem defined as Shift of Reference(SoR), which prevents accurate measurement of contact force. To overcome theproblem, we conduct the contact force estimation via standard deep neuralnetworks including for Recurrent Neural Network (RNN), Fully ConvolutionalNetwork (FCN) and Transformer. An average error in measurement for RNN, FCN andTransformer 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 systemresearch and open a publicly available dataset to public.

Roughly Collected Dataset for Contact Force Sensing Catheter