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Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis.
Rjoob, Khaled; Bond, Raymond; Finlay, Dewar; McGilligan, Victoria; Leslie, Stephen J; Rababah, Ali; Guldenring, Daniel; Iftikhar, Aleeha; Knoery, Charles; McShane, Anne; Peace, Aaron.
Afiliación
  • Rjoob K; Faculty of Computing, Engineering & Built Environment, Ulster University, UK. Electronic address: rjoob-k@ulster.ac.uk.
  • Bond R; Faculty of Computing, Engineering & Built Environment, Ulster University, UK.
  • Finlay D; Faculty of Computing, Engineering & Built Environment, Ulster University, UK.
  • McGilligan V; Faculty of Life & Health Sciences, Centre for Personalised Medicine, Ulster University, UK.
  • Leslie SJ; Department of Diabetes & Cardiovascular Science, University of the Highlands and Islands, Centre for Health Science, Inverness, UK.
  • Rababah A; Faculty of Computing, Engineering & Built Environment, Ulster University, UK.
  • Guldenring D; HTW Berlin, Wilhelminenhofstr. 75A, 12459 Berlin, Germany.
  • Iftikhar A; Faculty of Computing, Engineering & Built Environment, Ulster University, UK.
  • Knoery C; Department of Diabetes & Cardiovascular Science, University of the Highlands and Islands, Centre for Health Science, Inverness, UK.
  • McShane A; Emergency Department, Letterkenny University Hospital, Donegal, Ireland.
  • Peace A; Western Health and Social Care Trust, C-TRIC, Ulster University, UK.
J Electrocardiol ; 62: 116-123, 2020.
Article en En | MEDLINE | ID: mdl-32866909
ABSTRACT

INTRODUCTION:

Electrode misplacement and interchange errors are known problems when recording the 12­lead 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.

METHOD:

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.

RESULTS:

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.

CONCLUSION:

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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Electrocardiografía / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Qualitative_research / Systematic_reviews Límite: Humans Idioma: En Revista: J Electrocardiol Año: 2020 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Electrocardiografía / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Qualitative_research / Systematic_reviews Límite: Humans Idioma: En Revista: J Electrocardiol Año: 2020 Tipo del documento: Article