Abstract:
The Electrophysiology (EP) study is performed by pacing from localized areas within the
heart to detect different heart arrhythmia. On pacing, a patient, without any previous history, can
develop “Atrial Fibrillation” (AF). EP specialists identify AF by visual monitoring of intra
cardiac Electrograms (ICEMs) and experience. In order to proceed further with the diagnosis
and treatment, the patient has to be taken out of AF. In this thesis a real time algorithm is
developed for automatic detection of AF by analysis of ICEMs extracted from the High Right
Atrium (HRA) catheter. This new algorithm continuously monitors ICEMs during a standard EP
study. This work identifies two parameters, dominant frequency (DF) and Average power
spectral ratio (APSR) from estimated power spectral density (PSD) of ICEMs. The ICEMs used
are pre-processed for spectral estimation analysis. Non parametric power spectral estimation
methods have been used detecting DF. APSR has been used to ensure the reliability of DF peak,
and to differentiate between the atrial tachycardia, atrial flutter, and atrial fibrillation in their
overlapping frequency ranges. The algorithm has been successfully tested on 2000 events, and
the results have been validated by classified EP specialist.
