Variability in the atrial flutter vectorcardiographic loops and non-invasive localization of circuits

• Published in: Biomedical signal processing and control 2021-04, Vol.66, p.102472, Article 102472
• Main Authors: Kamarul Azman, Muhammad Haziq, Meste, Olivier, Kadir, Kushsairy, Laţcu, Decebal Gabriel, Saoudi, Nadir, Bun, Sok-Sithikun
• Format: Article
• Language: English
• Subjects: Arrhythmia; Bioengineering; Engineering Sciences; Life Sciences; Machine learning; Respiratory motion estimation; Signal and Image processing; Variability; Vectorcardiographic loop; Vectorcardiography
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102472

•It is possible to discriminate right and left atrial flutter localization by extracting and analyzing the variability contained inside atrial vectorcardiograms.•An original approach is provided for the general feature selection problem.•The classical respiratory motion estimation technique from the VCG is improved by using an original approach. Localization of atrial flutter circuits is tedious. Knowledge of this information beforehand will aid clinicians assess and plan ablation operations in advance to improve efficacy. In this article, we develop a novel classifier to determine flutter circuit localization by exploiting the variability contained in beat-to-beat series of vectorcardiographic flutter loop parameters, related to the variability of the activation circuit. Resulting classifier performance is satisfactory (maximum accuracy 0.93 for sample size of 56, sensitivity and specificity of 0.87 and 1.00 using logistic regression). Using an original method for selection of the most relevant features, it is shown that right and left AFL variability is different (right AFL has larger variability compared to left AFL) regarding higher-order statistics. On the other hand, it is hypothesized that respiratory motion alongside physical location of the two atria introduces variability in different amounts in right and left flutter circuits. This constitutes a confounding source of variability and may explain differences between right and left variability. We verify this hypothesis by removing this variability related to respiratory motion and analyzing the changes in classifier performance and variability feature values. It was concluded that respiratory motion is not the origin of discriminatory variability, and that this variability possibly originates from the AFL itself.