Rjoob Khaled, Bond Raymond, Finlay Dewar, McGilligan Victoria, Leslie Stephen J, Rababah Ali, Guldenring Daniel, Iftikhar Aleeha, Knoery Charles, McShane Anne, Peace Aaron
Faculty of Computing, Engineering & Built Environment, Ulster University, UK.
Faculty of Computing, Engineering & Built Environment, Ulster University, UK.
J Electrocardiol. 2020 Sep-Oct;62:116-123. doi: 10.1016/j.jelectrocard.2020.08.013. Epub 2020 Aug 19.
Electrode misplacement and interchange errors are known problems when recording the 12‑lead electrocardiogram (ECG). Automatic detection of these errors could play an important role for improving clinical decision making and outcomes in cardiac care. The objectives of this systematic review and meta-analysis is to 1) study the impact of electrode misplacement on ECG signals and ECG interpretation, 2) to determine the most challenging electrode misplacements to detect using machine learning (ML), 3) to analyse the ML performance of algorithms that detect electrode misplacement or interchange according to sensitivity and specificity and 4) to identify the most commonly used ML technique for detecting electrode misplacement/interchange. This review analysed the current literature regarding electrode misplacement/interchange recognition accuracy using machine learning techniques.
A search of three online databases including IEEE, PubMed and ScienceDirect identified 228 articles, while 3 articles were included from additional sources from co-authors. According to the eligibility criteria, 14 articles were selected. The selected articles were considered for qualitative analysis and meta-analysis.
The articles showed the effect of lead interchange on ECG morphology and as a consequence on patient diagnoses. Statistical analysis of the included articles found that machine learning performance is high in detecting electrode misplacement/interchange except left arm/left leg interchange.
This review emphasises the importance of detecting electrode misplacement detection in ECG diagnosis and the effects on decision making. Machine learning shows promise in detecting lead misplacement/interchange and highlights an opportunity for developing and operationalising deep learning algorithms such as convolutional neural network (CNN) to detect electrode misplacement/interchange.
在记录12导联心电图（ECG）时，电极放置错误和互换错误是已知问题。自动检测这些错误对于改善心脏护理中的临床决策和治疗结果可能起着重要作用。本系统评价和荟萃分析的目的是：1）研究电极放置错误对心电图信号和心电图解读的影响；2）确定使用机器学习（ML）检测最具挑战性的电极放置错误；3）根据敏感性和特异性分析检测电极放置错误或互换的算法的机器学习性能；4）识别检测电极放置错误/互换最常用的ML技术。本评价分析了有关使用机器学习技术识别电极放置错误/互换准确性的当前文献。
检索包括IEEE、PubMed和ScienceDirect在内的三个在线数据库，共识别出228篇文章，同时从共同作者的其他来源纳入了3篇文章。根据纳入标准，选择了14篇文章。对所选文章进行定性分析和荟萃分析。
文章显示了导联互换对心电图形态的影响，进而对患者诊断产生影响。对纳入文章的统计分析发现，除左臂/左腿互换外，机器学习在检测电极放置错误/互换方面表现出较高的性能。
本评价强调了在心电图诊断中检测电极放置错误的重要性及其对决策的影响。机器学习在检测导联放置错误/互换方面显示出前景，并突出了开发和应用深度学习算法（如卷积神经网络（CNN））来检测电极放置错误/互换的机会。
