A deep learning approach identifies new ECG features in congenital long QT syndrome

Abstract

Background: Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routinely see patients with congenital LQTS and may not always recognize the accompanying ECG features. In addition, a proportion of disease carriers do not display obvious abnormalities on their ECG. Combined, this can cause underdiagnosing of this potentially life-threatening disease.

Methods: This study presents 1D convolutional neural network models trained to identify genotype positive LQTS patients from electrocardiogram as input. The deep learning (DL) models were trained with a large 10-s 12-lead ECGs dataset provided by Amsterdam UMC and externally validated with a dataset provided by University Hospital Leuven. The Amsterdam dataset included ECGs from 10000 controls, 172 LQTS1, 214 LQTS2, and 72 LQTS3 patients. The Leuven dataset included ECGs from 2200 controls, 32 LQTS1, and 80 LQTS2 patients. The performance of the DL models was compared with conventional QTc measurement and with that of an international expert in congenital LQTS (A.A.M.W). Lastly, an explainable artificial intelligence (AI) technique was used to better understand the prediction models.

Results: Overall, the best performing DL models, across 5-fold cross-validation, achieved on average a sensitivity of 84 ± 2%, 90 ± 2% and 87 ± 6%, specificity of 96 ± 2%, 95 ± 1%, and 92 ± 4%, and AUC of 0.90 ± 0.01, 0.92 ± 0.02, and 0.89 ± 0.03, for LQTS 1, 2, and 3 respectively. The DL models were also shown to perform better than conventional QTc measurements in detecting LQTS patients. Furthermore, the performances held up when the DL models were validated on a novel external cohort and outperformed the expert cardiologist in terms of specificity, while in terms of sensitivity, the DL models and the expert cardiologist in LQTS performed the same. Finally, the explainable AI technique identified the onset of the QRS complex as the most informative region to classify LQTS from non-LQTS patients, a feature previously not associated with this disease.

Conclusions: This study suggests that DL models can potentially be used to aid cardiologists in diagnosing LQTS. Furthermore, explainable DL models can be used to possibly identify new features for LQTS on the ECG, thus increasing our understanding of this syndrome.

Keywords: Deep learning; ECG; Explainable AI; LQTS.

Fig. 1

Study design for LQTS ECG classification. A Schematic representation of the implemented pipeline. B Proposed 1DCNN architecture. C Strategy used to train, validate, and test the DL models

Fig. 2

Identification of ECG features importance. Box plots showing the score Grad-CAM corresponding to the P wave, QRS complex, and the S segment with the T wave calculated for 100 control ECGs correctly classified by the best performing DL models developed for A LQTS1, B LQTS2, and C LQTS3 ECG classification. *** Adjusted p-values ≤.001

Fig. 3

QRS complex comparison. The median QRS complex of 100 control ECGs (black lines) was retrieved and compared to the median QRS complex of the corresponding LQTS1/2/3 ECGs (green lines) analyzed by the best performing DL models (left). The median QRS complex from the control ECGs and LQTS ECGs was then calculated (right). On the x-axis data points from the waveform are shown; 25 data points correspond to 0.10 s or 100 ms