RESUMO
A method is presented for classifying a single lead surface electrocardiogram recording from a Holter monitor as being from a subject with paroxysmal atrial fibrillation (PAF) or not. The technique is based on first assessing the likelihood of 30-min segments of electrocardiogram (ECG) being from a subject with PAF, and then combining these per-segment likelihoods to form a per-subject classification. The per-segment assessment is based on the output of a supervised linear discriminant classifier (LDC) which has been trained using known data from the Physionet Atrial Fibrillation Prediction Database (which consists of two hundred 30-min segments of Holter ECG, taken from 53 subjects with PAF, and 47 without). One of two LDCs is used depending on whether there is a significant correlation between observed low-frequency and high-frequency spectral power in the RR power spectral density over the 30-min segment. If there is high correlation, then the LDC uses spectral features calculated over a 10-min window; in the low-correlation case, both spectral features and atrial premature contractions are used as features. The classifier was tested for its ability to distinguish PAF and non-PAF segments using three independent data sets (representing a total of 1370 segments from 50 subjects). The cumulative sensitivity, specificity, and accuracy on a per-segment basis were 43.0, 99.3, and 80.5%, respectively on these independent test sets. By combining the results of segment classification, a per-subject classification into PAF and non-PAF classes was performed. For the 50 subjects in the independent data sets, the sensitivity and specificity of the per-subject classifier were 100%.
