A novel application of sample entropy to the electrocardiogram of atrial fibrillation

Nowadays, several non-linear regularity estimators have been successfully applied to invasive atrial electrograms in order to characterize the atrial electrical activity organization during atrial fibrillation (AF). This arrhythmia is the most common encountered in clinical practice, accounting for approximately one-third of all the hospitalizations for cardiac rhythm disturbances. However, from a clinical point of view, it would be more desired to evaluate atrial activity (AA) organization from surface electrocardiographic (ECG) recordings, since they can be obtained easily and cheaply and the risks associated with invasive recordings could be avoided. In this work, Sample Entropy (SampEn) is proposed to assess the organization degree of the AA obtained from surface ECGs. To this respect, a reliable and non-invasive organization estimator would allow the prediction of spontaneous AF termination, since invasive studies have shown more organized electrical activity signals during the preceding instants of AF termination. The proposed method computed SampEn over the AA obtained from TQ segments, free of QRST complexes, and was validated with a database containing a training set of 20 AF recordings, with known termination properties, and a test set of 30 recordings. A simulation study showed that patients with heart rates of 130 bpm and above must be handled with care, because TQ intervals could be considerably reduced (<50 ms). As an overall result, spontaneous AF termination in 90% of the learning and test recordings was correctly predicted through this novel approach. As a conclusion, this work introduces the application of a non-linear regularity index able to assess significative differences in AA organization from surface ECG recordings during AF.