Joint cardiac tissue conductivity and activation time estimation using confirmatory factor analysis

• Published in: Computers in biology and medicine 2022-05, Vol.144, p.105393-105393, Article 105393
• Main Authors: Sun, Miao, de Groot, Natasja M.S., Hendriks, Richard C.
• Format: Article
• Language: English
• Subjects: Abnormalities; Activation time estimation; Algorithms; Anisotropy; Atrial Fibrillation - diagnosis; Cardiac arrhythmia; Conductivity; Conductivity estimation; Confirmatory factor analysis; Cross power spectral density; Electric Conductivity; Electrodes; Electrophysiology; Errors; Factor analysis; Factor Analysis, Statistical; Fibrillation; Heart; Heart Atria; Heart Rate; Humans; Mathematical models; Parameter estimation; Power spectral density; Propagation; Robustness (mathematics); Spatial data; Substrates; Tissues
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.105393

Mathematical models of the electrophysiology of cardiac tissue play an important role when studying heart rhythm disorders like atrial fibrillation. Model parameters such as conductivity, activation time, and anisotropy ratio are useful parameters to determine the arrhythmogenic substrate that causes abnormalities in the atrial tissue. Existing methods often estimate the model parameters separately and assume some of the parameters to be known as a priori knowledge. In this work, we propose an efficient method to jointly estimate the parameters of interest from the cross power spectral density matrix (CPSDM) model of the electrograms. By applying confirmatory factor analysis (CFA) to the CPSDMs of multi-electrode electrograms, we can make use of the spatial information of the data and analyze the relationship between the desired resolution and the required amount of data. With the reasonable assumptions that the conductivity parameters and the anisotropy parameters are constant across different frequencies and heart beats, we estimate these parameters using multiple frequencies and multiple heart beats simultaneously to easier satisfy the identifiability conditions in the CFA problem. Results on the simulated data show that using multiple heart beats decreases the estimation errors of the conductivity and the estimated activation time parameters. The experimental results on clinical data show that using multiple heart beats for parameter estimation can reduce the reconstruction errors of the clinical electrograms, which further demonstrates the robustness of the proposed method.