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Detecting ECG limb lead-wire interchanges involving the right leg lead-wire

机译:检测涉及右腿导线的ECG肢体导线互换

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ECG lead-wire interchanges involving the right leg (RL) are not always detected. These RL lead-wire interchanges cannot be simulated in the same way as other lead-wire interchanges making database collection a necessity for algorithm development. Adult 12-lead ECGs from a single teaching hospital taken between January 2008 and July 2012 were reviewed for lead-wire interchanges by an expert electrocardiographer. Lead-wire interchanges were confirmed by comparison of serial ECGs. Positive interchanges included left arm / right leg (flat lead III, n=134) and right arm / right leg (flat lead II, n=139). A RL lead-wire interchange algorithm was developed by bootstrap aggregation of decision trees with 5-fold cross validation. Test results were summed over the 5-fold cross validation on the partitions not used for training. ECG features included maximum and minimum QRS and T-wave voltages for ECG leads I, II and III. The Haisty algorithm for RA-RL interchange was tested for comparison. Algorithm performance was quantified by sensitivity (SE), specificity (SP) and estimated positive predictive value (PPV) based on SE, SP and realistic prevalence. For a prevalence of 0.2%, performance in SE, SP and PPV was: Haisty, 94, 99.4, 24; tree RA-RL: 84, 99.9, 57; tree LA-RL: 87, 99.9, and 57%. Even though SP was high for all three algorithms, the estimated PPVs were modest due to the low prevalence. Conclusion: Lead-wire interchanges involving the right leg wire can be detected with good sensitivity and high specificity. The higher specificity of the tree based algorithms results in more than twice the PPV of the Haisty algorithm.
机译:有时不会检测到涉及右腿(RL)的ECG导线互换。这些RL导线互换不能像其他导线互换一样模拟,这使得数据库收集成为算法开发的必要条件。在2008年1月至2012年7月之间从一家教学医院采集的成人12导联心电图经过专家心电图专家的检查,以进行导线互换。通过比较串行ECG确认了导线互换。正面互换包括左臂/右腿(扁平导线III,n = 134)和右臂/右腿(扁平导线II,n = 139)。通过引导树的决策树聚合和5倍交叉验证,开发了RL导线互换算法。在不用于训练的分区上,通过5倍交叉验证对测试结果求和。 ECG功能包括ECG导线I,II和III的最大和最小QRS和T波电压。测试了用于RA-RL互换的Haisty算法以进行比较。通过敏感性(SE),特异性(SP)和基于SE,SP和实际患病率的估计阳性预测值(PPV)量化算法性能。以0.2%的患病率,SE,SP和PPV的表现为:Haisty,94,99.4,24;树RA-RL:84、99.9、57;树LA-RL:87、99.9和57%。尽管这三种算法的SP都很高,但由于患病率较低,因此估计的PPV仍然适中。结论:可以检测到涉及右腿线的引线互换,并具有良好的灵敏度和高特异性。基于树的算法的较高特异性导致Haisty算法的PPV超过两倍。

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