Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA.
Physiol Meas. 2012 Sep;33(9):1549-61. doi: 10.1088/0967-3334/33/9/1549. Epub 2012 Aug 17.
Artifacts in an electrocardiogram (ECG) due to electrode misplacement can lead to wrong diagnoses. Various computer methods have been developed for automatic detection of electrode misplacement. Here we reviewed and compared the performance of two algorithms with the highest accuracies on several databases from PhysioNet. These algorithms were implemented into four models. For clean ECG records with clearly distinguishable waves, the best model produced excellent accuracies (> = 98.4%) for all misplacements except the LA/LL interchange (87.4%). However, the accuracies were significantly lower for records with noise and arrhythmias. Moreover, when the algorithms were tested on a database that was independent from the training database, the accuracies may be poor. For the worst scenario, the best accuracies for different types of misplacements ranged from 36.1% to 78.4%. A large number of ECGs of various qualities and pathological conditions are collected every day. To improve the quality of health care, the results of this paper call for more robust and accurate algorithms for automatic detection of electrode misplacement, which should be developed and tested using a database of extensive ECG records.
心电图（ECG）中的电极放置不当导致的伪影可能会导致误诊。已经开发出各种用于自动检测电极放置错误的计算机方法。在这里，我们回顾并比较了 PhysioNet 上几个数据库中准确率最高的两种算法的性能。这些算法被实现为四个模型。对于具有清晰可分辨波的干净 ECG 记录，对于除 LA/LL 互换（87.4%）之外的所有错位，最佳模型都产生了出色的准确率（>=98.4%）。然而，对于有噪声和心律失常的记录，准确率显著降低。此外，当在与训练数据库无关的数据库上测试算法时，准确率可能很差。在最坏的情况下，不同类型错位的最佳准确率范围从 36.1%到 78.4%。每天都会收集大量不同质量和病理条件的 ECG。为了提高医疗保健质量，本文的结果呼吁开发和测试更强大、更准确的用于自动检测电极放置错误的算法，该算法应使用广泛的 ECG 记录数据库进行开发和测试。
