Fusion of Multiple Univariate Data Analysis-based Detectors to Build a Specific Fingerprint of Atrial Fibrillation

Abstract

Automatic and fast atrial fibrillation (AF) diagnosis is still a major concern for the healthcare professional. Several algorithms based on univariate and multivariate analysis have been developed to detect AF. Although the published results do show satisfactory detection accuracy, computational complexity of such methods is still questionable. This study proposes an alternative way to diagnosis AF arrhythmia which is based on the combination of seven univariate data analysis-based detectors followed by a majority voting in order to build a digital fingerprint of AF. Four publicly-accessible sets of clinical data were used for AF assessment. The time series were segmented in 10 s RR interval window. The features of the four databases were merged in order to give rise huge variability and therefore to better characterize AF arrhythmia. Afterwards, a receiver operating characteristic curve analysis has been conducted to fix optimal thresholds for AF detection. Finally, the seven obtained detectors have been concatenated and then a majority rule was applied to yield a final decision on AF diagnosis. The results showed that this strategy performed better than some existing algorithms do, with 98.50% for sensitivity and 95.1 % specificity.