Abstract
Background
Brugada syndrome (BrS) risk stratification in asymptomatic subjects is still currently the most important yet unresolved clinical problem to determine the subset of patients with BrS requiring ICD implantation. The underlying pathophysiological mechanisms responsible for BrS ECG patterns remain unknown, as well as the mechanisms of the sudden onset of polymorphic ventricular tachycardia which leads to ventricular fibrillation and sudden cardiac death (SCD).
Purpose
This study aims to analyze from a totally alternative perspective, superficial 12-lead ECG signals. It departs from the numerous and various attempts to characterize and measure single morphology of specific and individual ECG segments, intervals and waves, rather focusing on and studying the dynamics and stability of the superficial 12-lead ECG signal as a whole to determine stability parameters able to contribute to BrS ECG pattern risk stratification and differential diagnosis of BrS.
Methods
A quantitative stability control closed loop system has been designed to model the electrophysiology dynamics of the cardiac conduction system with a 12-lead superficial ECG signal being the input and output of the system (Fig. 1). A normal ECG signal and a type-1 coved Brugada pattern ECG-V2 portion have been scanned, digitized and quantitatively processed to obtain stability parameters (poles and zeros in the S-plane). Scanning was performed by Digitizeit – Digital River GmbH. Processing in the S-plane was performed by ©2019 Wolfram Demonstrations Project & Contributors, http://demonstrations.wolfram.com/, poles and zeros and Microsoft Excel software was also used.
Results
Poles and zeros of the system for type-1 coved Brugada pattern ECG-V2 and for the normal ECG-V2 are shown in Fig. 2, together with stability.
Conclusions
Based on our data,
1. It appears that portions of the ECG patterns, approximated by mathematical continuous time models representing, at the infinitesimal limit, every possible pattern and behaviors of an ECG signal, such as repolarization patterns, may exhibit interesting dynamics characteristics of stability and can be stratified as stable, marginally stable or unstable.
2. Such a classification may then be implemented to risk stratify repolarization patterns. When tending to instability, such patterns seem to be associated to high risk repolarization patterns such as BrS coved type-1 pattern, hence indicating higher risk of developing polymorphic VT or SCD.
In conclusion, more work will be needed to further this methodology to improve the understanding of the effects of the various physiological and pathological substrates involved with malignant arrhythmias and to improve risk stratification strategies to determine the subset of patients with Brugada syndrome requiring ICD insertion. Control systems and stability theory may indeed indicate an interesting and effective procedure for future work.
Recognition and clinical implications of high prevalence of migraine in patients with Brugada syndrome and drug‐induced type 1 Brugada pattern
