Quantification of Ventricular Repolarization Dispersion Using Digital Processing of the Surface ECG

Digital processing of electrocardiographic records was one of the first applications of signal processing on medicine. There are many ways to analyze and study electrical cardiac activity using the surface electrocardiogram (ECG) and nowadays a good clinical diagnostic and prevention of cardiac risk are the principal goal to be achieved. One aim of digital processing of ECG signals has been quantification of ventricular repolarization dispersion (VRD), phenomenon which mainly is determined by heterogeneity of action potential durations (APD) in different myocardial regions. The APD differs not only between myocytes of apex and the base of both ventricles, but those of endocardial and epicardial surfaces (transmural dispersion) and between both ventricles. Also, it was demonstrated that several electrophysiologically and functionally different myocardial cells, like epicardial, endocardial and mid-myocardial M cells. The APD inequalities develop global and/or local voltage gradients that play an important role in the inscription of ECG T-wave morphology. In this way, we can assume that T-wave is a direct expression of ventricular repolarization inhomogeneities on surface ECG. Experimental and clinical studies have demonstrated a relationship between VRD and severe ventricular arrhythmias. In addition, patients having increased VRD values have a higher risk of developing reentrant arrhythmias. Frequently the heart answer to several pathological states produced an increase of VRD; this phenomenon may develop into malignant ventricular arrhythmia (MVA) and/or sudden cardiac death (SCD). Moreover, it has been showed that the underlying mechanisms in MVA and/or SCD are cardiac re-entry, increased automation, influence of autonomic nervous system and arrhythmogenic substrates linked with cardiac pathologies. These cardiac alterations could presented ischemia, hypothermia, electrolyte imbalance, long QT syndrome, autonomic system effects and others. Digital processing of ECG has been proved to be useful for cardiac risk assessment, with additional advantages like of being non invasive treatments and applicable to the general population. With the aim to identify high cardiac risk patients, the researchers have been tried to quantify the VRD with different parameters obtained by mathematic-computational processing of the surface ECG. These parameters are based in detecting changes of T-wave intervals and T-wave morphology during cardiac pathologies, linking these changes with VRD. In this chapter, we have presented a review of VRD indexes based on digital processing of ECG signals to quantify cardiac risk. The chapter is organized as follows: Section 2 explains ECG preprocessing and delineation of fiducial points. In Section 3, indexes of VRD quantification, such as: QT interval dispersion, QT interval variability and T-wave duration, are described. In Section 4, different repolarization indexes describing T-wave morphology and energy are examined, including complexity of repolarization, T-wave residuum, angle between the depolarization and repolarization dominant vectors, micro T-wave alternans, T-wave area and amplitude and T-wave spectral variability. Finally, in Section 5 conclusions are presented.Fil: Vinzio Maggio, Ana Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Bonomini, Maria Paula. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería; ArgentinaFil: Arini, Pedro David. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentin

Investigation of Absolute Refractory Period Pacing to Prevent Lethal Arrhythmias in Humans

Sudden cardiac death (SCD) is a major health issue, being the commonest cause of natural death in the industrialised world. SCD frequently results from the development of erratic heart rhythms which are usually preceded by repolarisation alternans (RA). Previous studies suggest that the abolishment of RA may prevent the onset of arrhythmia. In a recent swine study, absolute refractory period pacing (ARPP) showed promising results in RA modulation. However, the cellular mechanisms underlying this therapy and its efficiency in human patients remains unclear. Single cell in silico modelling showed that ARPP might be used to both increase or decrease action potential duration (APD) with the degree of modulation depending mainly on stimulus duration, magnitude and coupling interval. ICaL, IKr and IK1 were the main currents involved, and conductance of Ito and ICaL strongly influenced results. APD alternans was successfully reduced in a population of alternating models. In vivo results obtained using an epicardial sock during cardiac surgery showed significant changes in repolarisation when applying ARPP. However, elevated morphological signal alterations led to question the results’ validity. The investigation of signal processing methodology led to the acknowledgement of high-pass filter interference in signal morphology due to the ARPP artefact, resulting in altered markers. Further in vivo data showed no significant effect of ARPP on local RT at the whole heart level. Small effects on RT, spectral method and Tend markers close to the pacing site were observed, suggesting a localised effect. One dimensional in silico modelling showed a rapid decline of the ARPP effect, being limited to around 10mm from the pacing site, correlating with the in vivo results. These results provide important new knowledge regarding the effects of ARPP in the human ventricle at the cellular and organ level. It also provides relevant information for further development, analysis and translation of pacing based therapies

Classifying Mechanisms of Spiral Wave Breakup Underlying Cardiac Fibrillation Using Quantitative Metrics

Cardiovascular diseases are one of the leading causes of death in the world and manifest themselves in several forms, including arrhythmias. These disruptions in the normal rhythm of the heart inhibit the regular transmission of electrical signals that are essential for the heart to contract and pump blood to the rest of the body. During reentrant arrhythmias, spiral or scroll waves of electrical activation are conducted through the cardiac tissue and excite it repeatedly. As these waves propagate through the heart, they can break up in an irregular manner, leading to the onset of fibrillation. There are several mechanisms by which these reentrant waves can destabilize, but they are known mostly from computational studies. Experimentally, it has not been possible so far to distinguish among these mechanisms based on straightforward observations of the heart\u27s voltage during fibrillation. As a preliminary step in this direction, we aim to determine whether quantifying certain observable properties of the system will allow us to identify the mechanism underlying a given fibrillation episode. Toward this end, we propose a number of metrics that could help us classify mechanisms underlying fibrillation, including chaos in the system as assessed by the largest Lyapunov exponent; the amount of information (mutual information) and dependency (spatial correlation) shared by various spatial points in the domain; and reentrant wave properties like the number of reentries, wave birth and death rates, reentrant wave lifetimes, and spiral wave tip speeds. We implement and apply these metrics to simulated data obtained by numerically solving partial differential equations describing electrical wave propagation in the heart. Specifically, we analyze data achieved through six different mechanisms of reentrant wave breakup: steep APD restitution, discordant alternans, bistability, Doppler effect, supernormal conduction velocity and periodic boundary conditions. Our results suggest that of the various reentrant wave properties, the distribution of the number of reentries over time serves to be the most useful metric by providing a visual representation of how the breakup proceeds with time for each mechanism. When the mutual information and spatial correlation are studied in the context of the distribution of reentries over time, they help us gauge any spatial dependencies that may be present in the system. To validate our findings, we carried out a blind test to classify breakup mechanisms in four provided data sets with established breakup mechanisms. Our metrics correctly classified the mechanisms for three of these cases, and we are condent that further optimization could improve the reliability of our approach. Our work forms the basis for future studies that apply these and other metrics towards identifying the mechanisms responsible for fibrillation in experimental settings