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Data driven feature selection and machine learning to detect misplaced V1 and V2 chest electrodes when recording the 12‑lead electrocardiogram

  • Background: Electrocardiogram (ECG) lead misplacement can adversely affect ECG diagnosis and subsequent clinical decisions. V1 and V2 are commonly placed superior of their correct position. The aim of the current study was to use machine learning approaches to detect V1 and V2 lead misplacement to enhance ECG data quality. Method: ECGs for 453 patients, (normal n = 151, Left Ventricular Hypertrophy (LVH) n = 151, Myocardial Infarction n = 151) were extracted from body surface potential maps. These were used to extract both the correct and incorrectly placed V1 and V2 leads. The prevalence for correct and incorrect leads were 50%. Sixteen features were extracted in three different domains: time-based, statistical and time-frequency features using a wavelet transform. A hybrid feature selection approach was applied to select an optimal set of features. To ensure optimal model selection, five classifiers were used and compared. The aforementioned feature selection approach and classifiers were applied for V1 and V2 misplacement in threeBackground: Electrocardiogram (ECG) lead misplacement can adversely affect ECG diagnosis and subsequent clinical decisions. V1 and V2 are commonly placed superior of their correct position. The aim of the current study was to use machine learning approaches to detect V1 and V2 lead misplacement to enhance ECG data quality. Method: ECGs for 453 patients, (normal n = 151, Left Ventricular Hypertrophy (LVH) n = 151, Myocardial Infarction n = 151) were extracted from body surface potential maps. These were used to extract both the correct and incorrectly placed V1 and V2 leads. The prevalence for correct and incorrect leads were 50%. Sixteen features were extracted in three different domains: time-based, statistical and time-frequency features using a wavelet transform. A hybrid feature selection approach was applied to select an optimal set of features. To ensure optimal model selection, five classifiers were used and compared. The aforementioned feature selection approach and classifiers were applied for V1 and V2 misplacement in three different positions: first, second and third intercostal spaces (ICS). Results: The accuracy for V1 misplacement detection was 93.9%, 89.3%, 72.8% in the first, second and third ICS respectively. In V2, the accuracy was 93.6%, 86.6% and 68.1% in the first, second and third ICS respectively. There is a noticeable decline in accuracy when detecting misplacement in the third ICS which is expected.show moreshow less

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Metadaten
Author:Daniel GüldenringORCiD, Khaled Rjoob, Raymond R. BondORCiD, Dewar D. FinlayORCiD, Victoria McGilligan, Stephen J. Leslie, Aleeha Iftikhar, Ali S. Rababah, Charles Knoery, Anne McShane, Aaron Peace
DOI:https://doi.org/10.1016/j.jelectrocard.2019.08.017
Identifier:0022-0736 OPAC HS OPAC extern
Parent Title (German):Journal of Electrocardiology
Publisher:Elsevier
Place of publication:New York
Document Type:Article
Language:English
Date of Publication (online):2019/08/24
Year of first Publication:2019
Tag:Biomedizinische Signalverarbeitung; EKG
ECG
Volume:57
Issue:November-December
Number of pages:6 Seiten
First Page:39
Last Page:43
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Publication Lists:Güldenring, Daniel
Publication reviewed:begutachtet
Release Date:2021/02/16
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