Abstract:

Atrial arrhythmias such as atrial flutter and atrial fibrillation are a burden for patients and a major challenge for modern healthcare systems. Identification of patients at risk to develop atrial arrhythmias at an early stage carries the potential to reduce the incidence by implementing appropriate strategies to mitigate the risks. Diagnostic methods based on the ECG are ideal risk markers due to their noninvasiveness and omnipresence. The left atrium (LA) plays a major role in the intiation and perpetuation of atrial reentry arrhythmias. However, the LA is not well represented in the P-wave derived through standard ECG leads. Here, we optimize ECG lead positions to maximize LA information content. Towards this end, we used a cohort of eight personalized computational models providing the unique opportunity to separate LA and right atrial (RA) contributions to the P-wave, which is not feasible in vivo. The location of maximum P-wave signal energy was located on the center of the chest for all subjects with marked overlap between regions of maximum LA and RA P-wave amplitude. The regions of highest ratio between LA and RA signal energy differed between patients. However, a region with LA signal energy being higher than that of the RA and providing a sufficiently large absolute P-wave amplitude was identified at the center of the back consistently across five models of the cohort. Optimized linear combinations of standard 12-lead signals yielded comparably good results. Our newly proposed electrode positions on the back as well as selected linear combinations of standard 12-lead signals improve the LA information content considerably. By using these, more relevant diagnostic information regarding the anatomical and electrophysiological properties of the LA can be derived in future.

Abstract:

Atrial fibrillation and atrial flutter are the most common atrial arrhythmias placing a heavy burden on patients and posing a challenge on healthcare systems. If patients at risk to develop atrial arrhythmias can be identified at an early stage, the arrhythmia incidence can be lowered by implementing appropriate strategies to mitigate the risks. Diagnostic methods based on the ECG are ideal risk markers due to their noninvasiveness and omnipresence. The left atrium (LA) plays a major role in the initiation and perpetuation of atrial reentry arrhythmias. However, the LA is not well represented in the P-wave derived through standard ECG leads. Here, we optimize ECG leads to maximize LA information content. Towards this end, we used a cohort of eight personalized computational models providing the unique opportunity to separate LA and right atrial (RA) contributions to the P-wave, which is not feasible in vivo. The location of maximum P-wave signal energy was located on the center of the chest for all subjects with marked overlap between regions of maximum LA and RA P-wave amplitude. The regions of highest ratio between LA and RA signal energy differed between patients. However, a region with LA signal energy being higher than that of the RA and providing a sufficiently large absolute P-wave signal energy was identified at the lower left quadrant of the back consistently across most subjects of the cohort. Optimized linear combinations of standard 12-lead signals (considering the eight independent leads) yielded comparably good results amplifying LA information by more than one order of magnitude. Our newly proposed electrode positions on the back as well as selected combinations of standard ECG signals improve the LA information content considerably. By using these, more relevant diagnostic information regarding anatomical and electrophysiological properties of the LA can be derived in future.

Abstract:

Atrial arrhythmias like atrial fibrillation (AFib) or atrial flutter (AFlut) are a major challenge for modern healthcare systems in terms of prevalence and high costs. AFib itself is the most common arrhythmia among humans that correlates with cardiac diseases like heart failure, sudden cardiac death, or stroke. Chronic AFib is associated with a fivefold increase of stroke occurence [1] and affects already more than 2.2 percent of the population, with an estimated increase of patients within the next years [2].The early detection of patients at risk becomes more and more important for the punctual treatment to reduce the incidence. The human body surface electrocardiogram (ECG) is a proved method for the early detection due to its routinely non-invasive use in a large number of examinations.In this work, three distinct topics were treated in order to improve the diagnostic potential of the ECG regarding the atria. First, simulated atrial body surface potential maps (BSPMs) of different anatomical models were analyzed to find an optimal lead configuration for maximal left atrial information. The resulting lead configurations were also investigated for robustness in clinical practice. Second, a low voltage area LVA was induced into the left atrium (LA) for the investigation of changes in Pwave morphology. Therefore, a modeling approach based on clinical examinations was used for the creation of the LVA. Lastly, the behavior of heterogeneous electrophysiological tissue properties was assessed under pacing from the sinoatrial node (SN) and the left atrial appendage (LAA) [3]. Here, the key components were the determination of the activation sequence and the investigation of the repolarization in different atrial regions.Lead positions maximizing the left atrial information differed between the models, neverthe- less an area slightly down left to the center of the back was found that was suitable for the usage in clinical practice. The created LVA model combined with the insertion of the LVA to the LA anterior heart wall resulted in insignificant small impacts on the body surface ECG. The investigation of the heterogeneous model pointed out that the last activated atrial areas occurred in the right atrium (RA) independent from the pacing position. Moreover it was shown that the repolarization sequence was prolonged in the case of pacing from the LAA. The results improved the understanding of the atrial electrophysiology and offer new insights into the examination and interpretation of the atrial P and TAwave of the ECG.