Noninvasive Assessment of the Complexity and Stationarity of the Atrial Wavefront Patterns During Atrial Fibrillation

Abstract

A novel automated approach to quantitatively evaluate the degree of spatio-temporal organization in the atrial activity (AA) during atrial fibrillation (AF) from surface recordings, obtained from body surface potential maps (BSPM), is presented. AA organization is assessed by measuring the reflection of the spatial complexity and temporal stationarity of the wavefront patterns propagating inside the atria on the surface ECG, by means of principal component analysis (PCA). Complexity and stationarity are quantified through novel parameters describing the structure of the mixing matrices derived by the PCA of the different AA segments across the BSPM recording. A significant inverse correlation between complexity and stationarity is highlighted by this analysis. The discriminatory power of the parameters in identifying different groups in the set of patients under study is also analyzed. The obtained results present analogies with earlier invasive studies in terms of number of significant components necessary to describe 95% of the variance in the AA (four for more organized AF, and eight for more disorganized AF). These findings suggest that automated analysis of AF organization exploiting spatial diversity in surface recordings is indeed possible, potentially leading to an improvement in clinical decision making and AF treatment.

Spatial Variability of the 12-Lead Surface ECG as a Tool for Noninvasive Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation

“…The derivation of PCs is based on the assumption that y AA (n) is a zero-mean random process. The entries of column vector m k weigh the contribution of the corresponding PC x k to the leads in y AA (n), and thus render the principal propagation direction or spatial topography of each source [17]. This decomposition effectively exploits spatial relationships among ECG leads and discards redundant information by keeping only the most significant PCs and their corresponding directions.…”

Study of New Criteria Based on Eigenvalue Decomposition to Assist Arrhythmogenic Cardiomyopathy Diagnosis

“…On the other hand, Principal Component Analysis (PCA) has been successfully used in ECG signal processing in different applications [7,8].…”

Wavelet Entropy as a Measure of Ventricular Beat Suppression from the Electrocardiogram in Atrial Fibrillation

“…The f-wave yields useful information about the underlying arrhythmia. For example, the atrial activation rate can be determined non-invasively rather than by invasive electrophysiological study [3,4], it contains information about the complexity of the underlying atrial propagation [5,6] and the effects of treatment can be quantified and predicted [7][8][9][10][11].…”