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

  • 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 V2Electrode 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.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, Aaron Peace
DOI:https://doi.org/10.22489/CinC.2019.035
Identifier:9781728169361 OPAC HS OPAC extern
Identifier:2325-887X OPAC HS OPAC extern
Parent Title (German):Computers in Cardiology (CinC)
Publisher:IEEE
Place of publication:Piscataway
Document Type:conference proceeding (volume)
Conference:2019 Computing in Cardiology (CinC), 8 - 11. Sept. 2019, Singapore
Language:English
Date of Publication (online):2019/10/01
Year of first Publication:2019
Volume:2019
Number of pages:4 Seiten
First Page:1
Last Page:4
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Research focus:Keine Forschung
Publication Lists:Güldenring, Daniel
Publication reviewed:begutachtet
Release Date:2021/02/17
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