Non-Invasive Assessment of Spatiotemporal Organization of Ventricular Fibrillation Through Principal Component Analysis

Abstract

Introduction Ventricular fibrillation (VF) is the main cause of sudden cardiac death, but we lack tools to predict the evolution of its complexity. This study proposes novel VF complexity markers obtained by principal component analysis (PCA) of body surface potential maps (BSPMs). Methods BSPMs were divided in 0.5-s segments, each projected on the 3D PCA subspace determined in the previous frame. Reconstruction error was expressed in terms of norm d and angle cosine cos(α), and the nondipolar component index (NDI) quantified the energy of the first 3 PCA eigenvalues. These markers quantified changes in complexity between the beginning and the end of 24 VF episodes and 5 control sinus rhythm (SR) recordings. They were also tested on 6 BSPMs from a torso-tank model during and after ex-vivo pig heart perfusion. Differences between in-silico BSPM right and left leads were then verified in 4 human VF simulations, with a reentrant source in the right ventricle. Results Higher NDI and d and lower cos(α) denoted higher complexity at the end of VF (p < 0.0001). No changes occurred in SR (p > 0.05). The indices also underlined higher disorganization in exvivo non-perfused VF (p < 0.0001). A similar trend was observed in the right in-silico BSPM leads (p < 0.05). Conclusions PCA can non-invasively quantify changes in VF complexity.

Noninvasive Assessment of Atrial Fibrillation Complexity in Relation to Ablation Characteristics and Outcome

“…We investigated whether we could measure AF complexity as a function of the ability of PCA to compress the input BSPM signal into a few components while retaining the maximum amount of information as measured by variance. To this end, BSPMs were divided in N s = 500-ms segments, and in each frame ( s ) singular value decomposition of the input data Y ( s ) was performed as in Bonizzi et al ( 2010 ); Meo et al ( 2013a , b , 2017 ):…”

“…This study takes a step from this research and puts forward a noninvasive PCA-based approach for the quantification of AF spatiotemporal complexity. Additionally, in Meo et al ( 2017 ) some PCA-derived parameters were developed to predict changes in body surface complexity during ventricular fibrillation episodes. In this study, a similar methodology is proposed to quantify the spatiotemporal organization of AF wavefront propagation pattern as measured on body surface potentials.…”

“…The research presented in this manuscript extends a study submitted to Heart Rhythm Society Congress 2018. Some methodological aspects have been also applied to ventricular fibrillation analysis and presented at Computing in Cardiology 2017 conference (Meo et al, 2017 ).…”

Atrial Fibrillation Spatiotemporal Complexity Is Affected by Pulmonary Vein Isolation

“…Multivariate measures of AF complexity were computed by PCA as in [17]. Body surface heart electrical activity can be modeled as a 3D dipole [18], and most of its energy can be well approximated by the 3 dominant PCA eigenvectors [19].…”

“…This study aims to investigate to which extent PVI affects AF organization measured from BSPMs. Variations in signal features determined by PCA as in [16,17] were linked to changes in AF complexity occurring during PVI. Our approach provides deeper insights into the efficacy and the relevance of this intervention to persistent AF treatment.…”