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Critical Reviews™ in Biomedical Engineering

Publication de 6  numéros par an

ISSN Imprimer: 0278-940X

ISSN En ligne: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

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An Artificial Neural Network for the Electrocardiographic Diagnosis of Left Ventricular Hypertrophy

Volume 28, Numéro 3&4, 2000, pp. 435-438
DOI: 10.1615/CritRevBiomedEng.v28.i34.140
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RÉSUMÉ

Objective: A neural network was constructed to predict the presence of left ventricular hypertrophy (LVH) using both clinical information and the electrocardiogram (ECG).
Design and setting: In this retrospective study of 317 adult male patients, clinical parameters were age and history/physical examination: normal, heart failure, LV outflow obstruction, mitral regurgitation or aortic regurgitation. Multiple ECG parameters were used. A back-propagation neural network was constructed. The network was trained on 217 patients. A test set of 100 patients was then evaluated. The network was used to predict both LV mass and LVH by the criterion of LV mass index > 132 g/m2.
Results: LV mass was predicted with an accuracy of 79%. In predicting LVH, the network showed 82% correct diagnosis, sensitivity 94%, and specificity 65%. Positive predictive accuracy was 81% and negative predictive accuracy was 89%.
Conclusions: The neural network integrates clinical and ECG data and its resultant prediction of LVH is superior to that obtained using conventional ECG diagnostic criteria.

CITÉ PAR
  1. Haq Ikram U, Chhatwal Karanjot, Sanaka Krishna, Xu Bo, Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects, Vascular Health and Risk Management, Volume 18, 2022. Crossref

  2. Karatzia Loucia, Aung Nay, Aksentijevic Dunja, Artificial intelligence in cardiology: Hope for the future and power for the present, Frontiers in Cardiovascular Medicine, 9, 2022. Crossref

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