Abstract:
Electrophysiology study (EPS) has been serving as a diagnostic and curative
tool for rhythm-related cardiac diseases for more than few decades now. Clinical EPS
requires intense knowledge of numerous procedures and protocols and run-time
analyses of various parameters of the intracardiac signals in order to find the rootcause of the problem. The Intracardiac Electrograms available during EPS are
investigated for abnormality in cardiac activations. The specific manner in which the
abnormal activations occur gives hint of a particular disease. This crucial diagnostic
procedure involves manual calculations and thus requires a high level of proficiency
and capability. In this thesis, an attempt has been made to facilitate the doctors by
reducing the manual working required for diagnosis through development of an
automated arrhythmia detection algorithm. The proposed algorithm will assist the
beginners and support the decisions of the experts of EPS. The algorithm is designed
to detect AtrioVentricular Reentrant Tachycardia (AVRT), a type of Supraventricular
Tachycardia (SVT). After studying the underlying medical mechanisms, exploring
the electrograms formed by reentrant circuit and analyzing temporal progression of
cardiac activations during AVRT, a novel technique exploiting the above
relationships is presented.
