Predicting termination of paroxysmal atrial fibrillation using empirical mode decomposition of the atrial activity and statistical features of the heart rate variability.

This paper presents an algorithm for predicting termination of paroxysmal atrial fibrillation (AF) attacks using features extracted from the atrial activity (AA) and heart rate variability (HRV) signals. First, AA signal was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition method. Then, power spectrums of the AA and its IMFs (second, third, and forth components) were obtained, and the peak frequency of the power spectral densities were extracted. These features were complemented with three additional features consisting of mean, skewness, and kurtosis of the HRV signal. These seven features were then reduced to only two features by the generalized discriminant analysis technique. This not only reduces the number of the input features but also increases the classification accuracy by selecting most discriminating features. Finally, a linear classifier was used to classify AF episodes from AF termination database. This database consists of three types of AF episodes: N type (non-terminated AF episode), S type (terminated 1 min after the end of the record), and T type (terminated immediately after the end of the record). The obtained sensitivity, specificity, positive predictivity, and negative predictivity were 94, 97, 92, and 96 %, respectively. The important advantage of the proposed method comparing to the other existing approaches is that our algorithm can simultaneously discriminate three types of AF episodes with high accuracy.