Jekova Irena, Krasteva Vessela, Leber Remo, Schmid Ramun, Twerenbold Raphael, Müller Christian, Reichlin Tobias, Abächerli Roger
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.
Comput Methods Programs Biomed. 2016 Oct;134:31-41. doi: 10.1016/j.cmpb.2016.06.003. Epub 2016 Jun 23.
A crucial factor for proper electrocardiogram (ECG) interpretation is the correct electrode placement in standard 12-lead ECG and extended 16-lead ECG for accurate diagnosis of acute myocardial infarctions. In the context of optimal patient care, we present and evaluate a new method for automated detection of reversals in peripheral and precordial (standard, right and posterior) leads, based on simple rules with inter-lead correlation dependencies.
The algorithm for analysis of cable reversals relies on scoring of inter-lead correlations estimated over 4s snapshots with time-coherent data from multiple ECG leads. Peripheral cable reversals are detected by assessment of nine correlation coefficients, comparing V6 to limb leads: (I, II, III, -I, -II, -III, -aVR, -aVL, -aVF). Precordial lead reversals are detected by analysis of the ECG pattern cross-correlation progression within lead sets (V1-V6), (V4R, V3R, V3, V4), and (V4, V5, V6, V8, V9). Disturbed progression identifies the swapped leads.
A test-set, including 2239 ECGs from three independent sources-public 12-lead (PTB, CSE) and proprietary 16-lead (Basel University Hospital) databases-is used for algorithm validation, reporting specificity (Sp) and sensitivity (Se) as true negative and true positive detection of simulated lead swaps. Reversals of limb leads are detected with Se = 95.5-96.9% and 100% when right leg is involved in the reversal. Among all 15 possible pairwise reversals in standard precordial leads, adjacent lead reversals are detected with Se = 93.8% (V5-V6), 95.6% (V2-V3), 95.9% (V3-V4), 97.1% (V1-V2), and 97.8% (V4-V5), increasing to 97.8-99.8% for reversals of anatomically more distant electrodes. The pairwise reversals in the four extra precordial leads are detected with Se = 74.7% (right-sided V4R-V3R), 91.4% (posterior V8-V9), 93.7% (V4R-V9), and 97.7% (V4R-V8, V3R-V9, V3R-V8). Higher true negative rate is achieved with Sp > 99% (standard 12-lead ECG), 81.9% (V4R-V3R), 91.4% (V8-V9), and 100% (V4R-V9, V4R-V8, V3R-V9, V3R-V8), which is reasonable considering the low prevalence of lead swaps in clinical environment.
Inter-lead correlation analysis is able to provide robust detection of cable reversals in standard 12-lead ECG, effectively extended to 16-lead ECG applications that have not previously been addressed.
正确解读心电图(ECG)的一个关键因素是在标准12导联心电图和扩展16导联心电图中正确放置电极,以便准确诊断急性心肌梗死。在优化患者护理的背景下,我们提出并评估一种基于导联间相关性依赖的简单规则自动检测外周导联和胸前(标准、右侧和后壁)导联反转的新方法。
电缆反转分析算法依赖于对来自多个心电图导联的时间相干数据在4秒快照上估计的导联间相关性进行评分。通过评估九个相关系数来检测外周电缆反转,即将V6与肢体导联(I、II、III、-I、-II、-III、-aVR、-aVL、-aVF)进行比较。通过分析导联组(V1-V6)、(V4R、V3R、V3、V4)和(V4、V5、V6、V8、V9)内的心电图模式互相关进展来检测胸前导联反转。进展异常可识别出互换的导联。
一个测试集,包括来自三个独立来源——公共12导联(PTB、CSE)和专有16导联(巴塞尔大学医院)数据库的2239份心电图——用于算法验证,报告特异性(Sp)和敏感性(Se),作为对模拟导联互换的真阴性和真阳性检测。当右下肢参与反转时,肢体导联反转的检测敏感性为95.5-96.9%,当右下肢参与时为100%。在标准胸前导联的所有15种可能的两两反转中,相邻导联反转的检测敏感性为:V5-V6为93.8%,V2-V3为95.6%,V3-V4为95.9%,V1-V2为97.1%,V4-V5为97.8%,对于解剖学上距离更远的电极反转,敏感性增加到97.8-99.8%。四个额外胸前导联的两两反转检测敏感性为:右侧V4R-V3R为74.7%,后壁V8-V9为91.4%,V4R-V9为93.7%,V4R-V8、V3R-V9、V3R-V8为97.7%。真阴性率更高,标准12导联心电图的特异性>99%,V4R-V3R为81.9%,V8-V9为91.4%,V4R-V9、V4R-V8、V3R-V9、V3R-V8为100%,考虑到临床环境中导联互换的低发生率,这是合理的。
导联间相关性分析能够可靠地检测标准12导联心电图中的电缆反转,并有效地扩展到以前未涉及的16导联心电图应用。