Novel non-invasive algorithm to identify the origins of re-entry and ectopic foci in the atria from 64-lead ECGs: A computational study.
Novel non-invasive algorithm to identify the origins of re-entry and ectopic foci in the atria from 64-lead ECGs: A computational study.
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DOI：
10.1371/journal.pcbi.1005270
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发表时间：
2017-03
影响因子：
4.3
通讯作者：
Zhang H
中科院分区：
文献类型：
--
作者：
Alday EA;Colman MA;Langley P;Zhang H
心房快速性心律失常，例如心房颤动（AF），其特征在于心房中的不规则电活动，通常与由折返性涡卷波、多个小波的重复传导或快速局灶性活动所支撑的不稳定兴奋相关联。流行病学研究表明，发达国家的AF患病率增加与老龄化社会有关，强调需要有效的治疗方案。通常用于治疗AF的导管消融治疗需要关于心房电兴奋的空间信息。标准12导联心电图（ECG）提供了一种用于非侵入性地识别心律失常的存在的方法，这是由于与窦性心律相比与心房激动相关联的ECG信号的不规则性，但是在提供特定空间信息方面具有局限性。因此，迫切需要开发新的方法来识别和定位脑电兴奋的起源。有创方法提供了心房活动的直接信息，但可能引起临床并发症。非侵入性方法避免了这种并发症，但由于监测的非直接性质，它们的发展带来了更大的挑战。基于多个导联（例如，64导联背心）中的ECG信号的算法可以提供可行的方法。在这项研究中，我们使用了一个生物病理学上详细的模型，人类心房和躯干，以调查从64导联背心的心电图信号的形态和位置的快速心房兴奋所产生的快速局灶性活动和/或折返涡卷波的起源之间的相关性。一个焦点定位算法，然后从这个相关性构建。该算法的成功率分别为93%和76%，正确识别的空间分辨率为40 mm的局灶性和折返性激发的起源。通用方法允许其应用于任何多导联ECG系统。这代表了我们先前开发的算法的显著扩展，以预测与焦点活动相关的AF起源。房性快速性心律失常与折返性兴奋、多小波或快速局灶性活动引起的不规则兴奋波相关。确定不规则活动的起源可能对疾病的诊断和治疗至关重要。在侵入性和非侵入性方法提供用于这种识别的方法的情况下，两者都具有相关联的缺点。在这项研究中，我们使用了人体心房和躯干的生物病理学详细模型，开发了一种基于64导联背心的心电图（ECG）信号与快速局灶性和折返性兴奋位置之间相关性的算法。利用心房激动和ECG信号的特性，我们开发了一种焦点定位算法，该算法能够区分快速局灶性活动和以同一位置为中心的折返性涡卷波。基于模拟数据，该算法正确识别局灶性和折返性兴奋起源的成功率分别为93%和76%，区分局灶性和折返性兴奋的成功率为88%，无假阳性。继承了我们以前的算法，它也很容易推广到任何多导联心电图系统。
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. Atrial tachy-arrhythmias are associated with irregular excitation waves arising from re-entrant excitation, multiple wavelets or rapid focal activity. Identifying the origin of the irregular activity may be vital for diagnosis and treatment of the disorder. Where invasive and non-invasive methods provide approaches for such identification, both have associated disadvantages. In this study, we used a biophysically detailed model of the human atria and torso to develop an algorithm based on the correlation between the electrocardiogram (ECG) signal from a 64-lead vest and the location of rapid focal and re-entrant excitation. Using the properties of the atrial activation and the ECG signals, we developed a focus-location algorithm which is able to distinguish rapid focal activity from re-entrant scroll waves centred in the same location. Based on simulated data, the algorithm had success rates of 93% and 76% for correctly identifying the origin of focal and re-entrant excitation, respectively, and 88% for distinguish focal and re-entrant excitation, with no false positives. Inherited from our previous algorithm, it is also easily generalizable to any multi-lead ECG system.