Significance of Mixing Matrix Structure on Principal Component-Based Analysis of Atrial Fibrillation Body Surface Potential Maps

Atrial fibrillation (AF) type classifiers are still hardly accepted in clinical practice due to their invasive ap-proach. In this work a new automated method to assess noninvasively different AF types is presented, based on the high spatial resolution given by body surface poten-tial maps (BSPM). AF organization degree was assessed by its influence on the spatio-temporal pseudostationarity and complexity of a principal component analysis mixing matrix repeatedly derived along a BSPM recording. Sta-tionarity was analyzed in terms of ability of the mixing ma-trix derived for a specific recording segment to retrieve the AA components of subsequent segments, while complexity in terms of its number of significant components. Results show that AF organization is reflected in a greater pseudo-stationarity of the mixing matrix along the recordings and in a lower number of components needed to represent the AA, interpreted as a lower complexity in the underlying AA in patients with organized AF type I. 1