The Application of Non-invasive Inverse ECG Techniques in Cardiomyopathy

Abstract

The electrocardiogram (ECG) plays an important role in systematically assessing cardiac electrical function, but the standard 12-lead ECG only provides only a distant view on cardiac electrical activity. Using non-invasive inverse ECG techniques, additional detailed information on cardiac electrical activity can be obtained by linking cardiac electrical activity to anatomy to enable the identification of subtle disease progression in arrhythmogenic cardiomyopathy. Arrhythmogenic cardiomyopathy is characterized by structural and electrical myocardial remodeling and can manifest as a broad range of lethal phenotypes. In arrhythmogenic cardiomyopathy, electrical remodeling can precede structural and functional changes and sudden cardiac death can be the first disease manifestation. This highlights the need for accurate screening and risk-stratification strategies.

The first part of this thesis focusses on describing the optimization of a traditional inverse ECG technique to provide non-invasive insight in endocardial and epicardial cardiac electrical activity by combining 67-lead ECG data with patient specific CT/MRI-based anatomical models. To be able to identify early signs of arrhythmogenic cardiomyopathy development, accurate imaging of sinus rhythm is of importance. Therefore, in Chapter 2, we report on our work regarding the optimization of the inverse ECG technique for the estimation of sinus rhythm and report on its performance (Chapter 3) by comparing it to invasive local activation maps. With the incorporation of a subject-specific anatomy-based model of the His-Purkinje system a physiologically realistic and robust estimation of the ventricular activation sequence is obtained. The optimized inverse ECG technique detected local electrophysiological characteristics in the activation sequence in pathogenic variant carriers with and without any clinical signs of disease (Chapter 4). To further optimize the performance of the inverse ECG technique by developing a new method to model myocardial disease in ECG simulation in Chapter 5 to provide a realistic relation between ECG waveforms and underlying activation sequences.

As traditional inverse ECG techniques are mathematically complex and computationally demanding, we focus on CineECG, a new method to image key features of the activation sequence that are difficult to reliably obtain from the ECG. We conceptually validated the technique in cases of bundle branch blocks (Chapter 6) and after evaluation, the method was optimized and validated through a simulation study (Chapter 7).

As accurate assessment of subtle ECG changes is limited due to inconsistencies in electrode positioning, we focused on the optimization of the 12-lead ECG acquisition by introducing a 3D-camera based method to reduce electrode placement misplacement (Chapter 8). With this new technique, the identification of subtle changes in the QRS complex during arrhythmogenic cardiomyopathy follow-up may be improved. In Chapter 9, we describe how novel AI-based algorithms may aid current clinical practice together with its potential benefits and challenges. With such algorithms, the complex nature of disease progression in arrhythmogenic cardiomyopathy may be further unraveled.

To conclude the thesis, the application of techniques presented in this thesis to enhance diagnosis and risk-stratification in arrhythmogenic cardiomyopathy is described (Chapter 10). The techniques are viewed within the context of possible fields of application in current clinical practice.