Novel non-invasive algorithm to identify the origins of re-entry and ectopic foci in the atria from 64-lead ECGs: A computational study

Atrial tachy-arrhytmias, such as atrial fibrillation (AF), are characterised by irregular electrical activity in the atria, generally associated with erratic excitation underlain by re-entrant scroll waves, fibrillatory conduction of multiple wavelets or rapid focal activity. Epidemiological studies have shown an increase in AF prevalence in the developed world associated with an ageing society, highlighting the need for effective treatment options. Catheter ablation therapy, commonly used in the treatment of AF, requires spatial information on atrial electrical excitation. The standard 12-lead electrocardiogram (ECG) provides a method for non-invasive identification of the presence of arrhythmia, due to irregularity in the ECG signal associated with atrial activation compared to sinus rhythm, but has limitations in providing specific spatial information. There is therefore a pressing need to develop novel methods to identify and locate the origin of arrhythmic excitation. Invasive methods provide direct information on atrial activity, but may induce clinical complications. Non-invasive methods avoid such complications, but their development presents a greater challenge due to the non-direct nature of monitoring. Algorithms based on the ECG signals in multiple leads (e.g. a 64-lead vest) may provide a viable approach. In this study, we used a biophysically detailed model of the human atria and torso to investigate the correlation between the morphology of the ECG signals from a 64-lead vest and the location of the origin of rapid atrial excitation arising from rapid focal activity and/or re-entrant scroll waves. A focus-location algorithm was then constructed from this correlation. The algorithm had success rates of 93% and 76% for correctly identifying the origin of focal and re-entrant excitation with a spatial resolution of 40 mm, respectively. The general approach allows its application to any multi-lead ECG system. This represents a significant extension to our previously developed algorithms to predict the AF origins in association with focal activities.

房性快速性心律失常（如心房颤动，AF）以心房电活动紊乱为核心特征，通常伴随以折返螺旋波、多小波颤动传导或快速局灶活动为病理基础的不规则心房兴奋。流行病学研究显示，发达国家随社会老龄化进程，房颤患病率逐年攀升，凸显了开发高效治疗方案的迫切需求。导管消融作为房颤临床治疗的常用手段，需获取心房电兴奋的空间分布信息。标准12导联心电图（ECG）可通过对比窦性心律与心房激活相关的ECG信号异常，实现心律失常的非侵入性识别，但在提供精准空间定位信息方面存在显著局限。因此，亟需研发新型方法以识别并定位心律失常兴奋灶的起源位置。侵入性检查可直接获取心房活动信息，但可能引发临床并发症；非侵入性方法虽可规避此类风险，但由于监测不具备直接性，其开发面临更大挑战。基于多导联（如64导联背心）ECG信号的算法或为可行的解决方案。本研究采用人体心房与躯干的生物物理精细模型，探究64导联背心采集的ECG信号形态与快速局灶活动和/或折返螺旋波引发的快速心房兴奋灶起源位置之间的相关性，并基于该相关性构建了病灶定位算法。该算法在空间分辨率为40 mm的条件下，对局灶性兴奋和折返性兴奋起源的正确识别率分别为93%和76%。本研究所用的通用方法可适配任意多导联ECG系统，相较我们此前针对局灶活动相关房颤起源预测的算法，实现了显著拓展。