Comparative study of non-invasive organization estimation strategies to predict spontaneous termination of atrial fibrillation

摘要

In the present work, three methods based on the Sample Entropy (SampEn) non-invasive organization estimation of atrial fibrillation (AF) to predict its spontaneous termination are compared making use of the same patient's database. In the first strategy, the atrial activity (AA) is obtained through QRST cancellation. Next, the main atrial wave (MAW) of the AA is obtained by selective filtering centered on the dominant atrial frequency (DAF), thus yielding the time series for SampEn computation. In the second strategy, an equivalent wave to the MAW is obtained by applying seven levels of discrete wavelet decomposition to the AA. The sub-band containing the DAF is reconstructed back to time domain and evaluated with SampEn. In the last strategy, the time series is obtained as the concatenation of TQ segments, free of QRST complexes. The three methods were validated with a database containing a training set of 20 AF recordings, with known termination properties, and a test set of 30 recordings. For the learning set, sensitivity values were 100%, 80%, and 80% and specificity values were 90%, 90%, 100% for the methods based on selective filtering, wavelet transform and concatenation of TQ segments, respectively. Regarding the test signals, a sensitivity of 93.75% and a specificity of 85.71% were provided for the three methods. These coherent outcomes allowed us to conclude that the three techniques can estimate robustly AF organization and predict successfully paroxysmal AF termination.