An artificial neural network for the electrocardiographic diagnosis of left ventricular hypertrophy.
OBJECTIVE
A neural network was constructed to predict the presence of left ventricular hypertrophy (LVH) using both clinical information and the electrocardiogram (ECG).


METHODS
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.
Hopkins
C B
CB
Division of Cardiology, University of South Carolina School of Medicine, Columbia 29203, USA.
Suleman
J
J
Cook
C
C
D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography; D006801:Humans; D017379:Hypertrophy, Left Ventricular / Q000175:diagnosis*; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity