Electrocardiographic Z-axis QRS-T voltage-time-integral in patients with typical right bundle branch block – Correlation with echocardiographic right ventricular size and function

Fairbank, Tyan; DeBauge, Ashley; Harvey, Christopher J.; Jiwani, Sania; Ranka, Sagar; Beaver, Timothy A.; Sheldon, Seth H.; Reddy, Madhu; Noheria, Amit

doi:10.1016/j.jelectrocard.2023.11.004

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2024

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Cited by 2 publications

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Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review

Ose,

Sattar,

Gupta

et al. 2024

Curr Cardiol Rep

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Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review

Ose,

Sattar,

Gupta

et al. 2024

Curr Cardiol Rep

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Machine learning to classify left ventricular hypertrophy using ECG feature extraction by variational autoencoder

Gupta,

Harvey,

DeBauge

et al. 2024

Preprint

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Background: Traditional ECG criteria for left ventricular hypertrophy (LVH) have low diagnostic yield. Machine learning (ML) can improve ECG classification. Methods: ECG summary features (rate, intervals, axis), R-wave, S-wave and overall-QRS amplitudes, and QRS/QRST voltage-time integrals (VTIs) were extracted from 12-lead, vectorcardiographic X-Y-Z-lead, and root-mean-square (3D) representative-beat ECGs. Latent features were extracted by variational autoencoder from X-Y-Z and 3D representative-beat ECGs. Logistic regression, random forest, light gradient boosted machine (LGBM), residual network (ResNet) and multilayer perceptron network (MLP) models using ECG features and sex, and a convolutional neural network (CNN) using ECG signals, were trained to predict LVH (left ventricular mass indexed in women >95g/m2, men >115g/m2) on 225,333 adult ECG-echocardiogram (within 45 days) pairs. AUROCs for LVH classification were obtained in a separate test set for individual ECG variables, traditional criteria and ML models. Results: In the test set (n=25,263), AUROC for LVH classification was higher for ML models using ECG features (LGBM 0.790, MLP 0.789, ResNet 0.788) as compared to the best individual variable (VTIQRS-3D 0.677), the best traditional criterion (Cornell voltage-duration product 0.647) and CNN using ECG signal (0.767). Among patients without LVH who had a follow-up echocardiogram >1 (closest to 5) years later, LGBM false positives, compared to true negatives, had a 2.63 (95% CI 2.01, 3.45)-fold higher risk for developing LVH (p<0.0001). Conclusions: ML models are superior to traditional ECG criteria to classify-and predict future-LVH. Models trained on extracted ECG features, including variational autoencoder latent variables, outperformed CNN directly trained on ECG signal.

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Electrocardiographic Z-axis QRS-T voltage-time-integral in patients with typical right bundle branch block – Correlation with echocardiographic right ventricular size and function

Cited by 2 publications

References 31 publications

Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review

Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review

Machine learning to classify left ventricular hypertrophy using ECG feature extraction by variational autoencoder

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