Analysis of Surface Atrial Signals: Time Series with Missing Data?

Uncovering of the atrial signal for patients undergoing episodes of atrial fibrillation is usually obtained from surface ECG by removing waves induced by ventricular activities. Once earned the atrial signal, the detection of the dominant fibrillation frequency is often the main (and only) goal. In this work we verified if subtraction of the ventricular activity might be avoided by performing spectral analysis on those ECG segments where ventricular activity is absent, (i.e. the T-Q intervals). While the approach might seem crude, in here the question was recast into a problem of missing data in a long time series and proper methods were applied: the Lomb periodogram and the iterative Singular Spectrum Analysis. The two methods were tested on both simulated signals and “realistic” atrial signals constructed using the ECG recordings provided by the 2004 Computers in Cardiology competition. The results obtained showed that both techniques were able to provide a reliable quantification of the dominant oscillation, with a slightly superior performance of the iterative Singular Spectrum Analysis. Absolute errors larger than 1.0 Hz were unlikely (p < 0.05) up to 130−140 bpm. Such level of agreement is consistent with similar comparative works where techniques for separating the atrial signal from ventricular waves were considered.