Machine Learning Improves the Detection of Misplaced V1 and V2 Electrodes During 12-Lead Electrocardiogram Acquisition

K. Rjoob, R. Bond, D. Finlay, V. Mcgilligan, Stephen Leslie, Aleeha Iftikhar, D. Guldenring, A. Rababah, C. Knoery, A. Peace
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Abstract

Electrode misplacement during 12-lead Electrocardiogram (ECG) acquisition can cause false ECG diagnosis and subsequent incorrect clinical treatment. A common misplacement error is the superior placement of V1 and V2 electrodes. The aim of the current research was to detect lead V1 and V2 misplacement using machine learning to enhance ECG data quality to improve clinical decision making. In this particular study, we reasonably assume that V1 and V2 are concurrently superiorly misplaced together. ECGs for 450 patients were extracted from body surface potential maps. Sixteen features were extracted including: morphological, statistical and time-frequency features. Two feature selection approaches (filter method and wrapper method) were applied to find an optimal set of features that provide a high accuracy. To ensure accuracy, six classifiers were applied including: fine tree, coarse tree, bagged tree, Linear Support Vector Machine (LSVM), Quadratic Support Vector Machine (QSVM) and logistic regression. The accuracy of V1 and V2 misplacement detection was 94.3% in the first ICS, 92.7% in the second ICS and 70% in third ICS respectively. Bagged tree was the best classifier in the first, second and third ICS to detect V1 and V2 misplacement.
机器学习改进了12导联心电图采集过程中V1和V2电极错位的检测
在12导联心电图采集过程中,电极错位会导致错误的心电图诊断和随后的错误临床治疗。常见的放置错误是V1和V2电极的优越放置。当前研究的目的是利用机器学习检测导联V1和V2错位,以提高ECG数据质量,从而改善临床决策。在这个特殊的研究中,我们合理地假设V1和V2同时优越地错位在一起。从体表电位图中提取450例患者的心电图。提取了16个特征,包括形态特征、统计特征和时频特征。采用两种特征选择方法(滤波法和包装法)来寻找提供高精度的最优特征集。为了保证分类精度,采用了细树、粗树、袋装树、线性支持向量机(LSVM)、二次支持向量机(QSVM)和逻辑回归六种分类器。第一次、第二次、第三次检测V1、V2错位的准确率分别为94.3%、92.7%和70%。套袋树是第一、第二和第三种ICS中检测V1和V2错位的最佳分类器。
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