Is the Dominant Frequency Accurate Enough for Atrial Fibrillation Signals?

Abstract

In noninvasive studies of atrial fibrillation (AF), especially in body surface potential map (BSPM) measurements, the dominant frequency (DF) is usually defined as the highest peak in the power spectrum, after prior cancellation or removal of the ECG components related to the ventricular activity. However, the power spectrum is often hampered by phase breaks presence in atrial signals due to either signal concatenation or to chaotic behavior. Fourier analysis (including multiple frequency components models) is used as a starting point to develop methods adapted to handle phase breaks. Fourier analysis and the average frequency derived from the phase of the analytic signal (within an AF cycle or globally) were selected as estimators of the single frequency model, and compared by means of simulations. It is found that for large phase breaks (±T/2 every half-second), and for a SNR of 5db, the 95 % confidence interval were smaller for the estimates based on the phase, within an AF cycle, of the analytic signal. For the more realistic multiple frequency model, the Fourier decomposition is extended by using a Least Mean Squares (LMS) adaptive algorithm, with or without imposing a constant magnitude. Slight differences in performances of the presented methods are exemplified on a single AF subject where the DF is computed over all the leads of the BSPM records.