Open AccessComparison of computation time for estimation of dominant frequency of atrial electrograms: Fast fourier transform, blackman tukey, autoregressive and multiple signal classification
Author(s) -
Anita Ahmad,
Fernando S. Schlindwein,
G A Ng
Publication year - 2010
Publication title -
journal of biomedical science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1937-688X
pISSN - 1937-6871
DOI - 10.4236/jbise.2010.39114
Subject(s) - fast fourier transform , autoregressive model , computation , signal (programming language) , time–frequency analysis , computer science , algorithm , spectral density estimation , fourier transform , mathematics , fourier analysis , speech recognition , pattern recognition (psychology) , statistics , artificial intelligence , computer vision , filter (signal processing) , mathematical analysis , programming language
Dominant frequency (DF) of electrophysiological data is an effective approach to estimate the activation rate during Atrial Fibrillation (AF) and it is important to understand the pathophysiology of AF and to help select candidate sites for ablation. Frequency analysis is used to find and track DF. It is important to minimize the catheter insertion time in the atria as it contributes to the risk for the patients during this procedure, so DF estimation needs to be obtained as quickly as possible. A comparison of computation tim- es taken for spectrum estimation analysis is presented in this paper. Fast Fourier Transform (FFT), Blackman-Tukey (BT), Autoregressive (AR) and Multiple Signal Classification (MUSIC) methods are used to obtain the frequency spectrum of the signals. The time to produce DF was measured for each method. The method which takes the shortest time for analysis is selected for real time application purpose