Publication de 6 numéros par an
ISSN Imprimer: 0278-940X
ISSN En ligne: 1943-619X
Indexed in
An Artificial Neural Network for the Electrocardiographic Diagnosis of Left Ventricular Hypertrophy
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.
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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
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