Classification of Fibrillation Organisation Using Electrocardiograms to Guide Mechanism-Directed Treatments

Abstract

Background: Atrial fibrillation (AF) and ventricular fibrillation (VF) are complex heart rhythm disorders and may be sustained by distinct electrophysiological mechanisms. Disorganised self-perpetuating multiple-wavelets and organised rotational drivers (RDs) localising to specific areas are both possible mechanisms by which fibrillation is sustained. Determining the underlying mechanisms of fibrillation may be helpful in tailoring treatment strategies. We investigated whether global fibrillation organisation, a surrogate for fibrillation mechanism, can be determined from electrocardiograms (ECGs) using band-power (BP) feature analysis and machine learning.Methods: In this study, we proposed a novel ECG classification framework to differentiate fibrillation organisation levels. BP features were derived from surface ECGs and fed to a linear discriminant analysis classifier to predict fibrillation organisation level. Two datasets, single-channel ECGs of rat VF (n = 9) and 12-lead ECGs of human AF (n = 17), were used for model evaluation in a leave-one-out (LOO) manner.Results: The proposed method correctly predicted the organisation level from rat VF ECG with the sensitivity of 75%, specificity of 80%, and accuracy of 78%, and from clinical AF ECG with the sensitivity of 80%, specificity of 92%, and accuracy of 88%.Conclusion: Our proposed method can distinguish between AF/VF of different global organisation levels non-invasively from the ECG alone. This may aid in patient selection and guiding mechanism-directed tailored treatment strategies.

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

“…Lee et al (2013) introduced a novel method that calculated time‐varying coherence function values from two time‐varying transfer functions, which measure frequency variations between two adjacent beat segments, to detect AF. An approach proposed by X. Li et al (2021) introduced a classification framework that distinguishes different levels of fibrillation organization by utilizing band‐power features extracted from ECG signals. These features were then inputted into a linear discriminant analysis classifier to predict the occurrence of AF.…”

“…Hurnanen et al (2017) also developed a hybrid model to detect AF using time‐frequency analysis of seismocardiograms, which are vibrations generated by the heart recorded using body surface accelerometers. X. Lai et al (2021) proposed a CNN‐SVM model the predicted for AF recurrence based on preoperative AF signals, improving on the performance for differentiating linear versus nonlinear data compared with multilevel perception‐based.…”

“…An approach proposed by X. Li et al (2021) introduced a classification framework that distinguishes different levels of fibrillation organization by utilizing band-power features extracted from ECG signals.…”