Key result
Autoregressive spectral estimation combined with spread spectrum analysis demonstrated advantages over established Fourier analysis algorithms for evaluating cardiac electrogram recordings.
Autoregressive spread-spectral analysis offers advantages over traditional Fourier analysis for evaluating complex intracardiac electrograms such as fibrillation.
May refine spectral analysis of complex electrograms; leaves open clinical adoption pending validation.
The combination of autoregressive (AR) spectral estimation and the spread spectrum is investigated as a method of evaluating cardiac electrogram recordings. The authors provide examples of autoregressive spectral estimation applied to nonfibrillatory and fibrillatory signals and demonstrate its advantages over established spectral analysis algorithms. AR models were fit to brief epochs of atrial fibrillation, ventricular fibrillation, sinus rhythm, and other nonfibrillatory rhythm electrograms. Spread-spectrum plots were constructed from epoch spectra to examine temporal changes in cardiac dynamics as well as differences in spectral estimates. The results of applying the high resolution spread spectrum to clinical data in relation to proposed physiological mechanisms of fibrillation are examined.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Lovett et al. (2003) studied Cardiac arrhythmias (atrial fibrillation, ventricular fibrillation). Autoregressive (AR) spectral estimation and spread spectrum vs. Fourier analysis / established spectral analysis algorithms was evaluated on Evaluation of cardiac electrogram recordings and temporal changes in cardiac dynamics. Autoregressive spectral estimation combined with spread spectrum analysis demonstrated advantages over established Fourier analysis algorithms for evaluating cardiac electrogram recordings.
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