Time and frequency series combination for non-invasive regularity analysis of atrial fibrillation.

In this work, we present a new method based on electrocardiogram signal processing to distinguish between the atrial fibrillation (AF) episodes that terminate immediately and those that sustain. The spectrogram of the atrial activity is computed and 12 numerical series of spectral parameters are constructed. The sample entropy (SampEn) of six series are relevant in the characterization of AF termination (p < 0.05). Furthermore, a combined discriminant analysis in both time and frequency domains is performed, which improves the univariant time-frequency analysis. The discriminant analysis achieves optimal combination of parameters so that the percentage of correctly classified recordings reaches 100% for the learning set and 93.33% for the test set. The main conclusion is that the combined analysis of time and frequency series regularity might be used to predict spontaneous termination of paroxysmal AF and could provide information about the organization of atrial activation in AF.