Life-threatening Ventricular Arrhythmia Prediction in Patients with Dilated Cardiomyopathy Using Explainable Electrocardiogram-Based Deep Neural Networks

Introduction: Patients with non-ischemic DCM are at risk of life-threatening ventricular arrhythmias. Improved patient selection for ICD implantation is warranted. Deep neural networks (DNN) can discover complex ECG features without the need for hand-crafted feature extraction. This study aimed to distinguish patients at risk of arrhythmias and detect and visualize ECG characteristics that attribute to the DNN classification. Methods: We trained a DNN model on baseline raw ECG samples of adult patients with DCM for the composite outcome of sustained ventricular arrhythmia, (aborted) sudden cardiac death and appropriate ICD therapy in the University Medical Centre Utrecht. Gradient-Weighted Class Activation Mapping was used for visual interpretation of the network. DNN performance was validated both internally and in an external cohort from the Maastricht University Medical University Center. Results: 26.628 ECGs were used for pre-training the network. In total, 317 patients with DCM were included in the primary cohort with a median age of 52 years (IQR 42 - 61), median LVEF of 25% (IQR 20-33), NYHA II and III (32% and 25%). 233 patients were ICD recipients. Subsequently 84 patients reached the outcome (median follow-up duration of 3.96 years (IQR 1.94 - 7.00)). The DNN focused on subtle but predictive differences in the terminal QRS-segment, the T-wave, and the P-wave (figure). The AUC of the DNN was 0.70 in cross-validation. External validation was similar, with an AUC of 0.66. Conclusion: In this study we trained and validated a deep neural network for prediction of life-threatening arrhythmic events. Even though the discriminatory changes on ECG segments were very subtle, the trained DNN was able to predict life-threatening events, focusing on the terminal QRS-segment, the T-wave, and the P-wave. Future studies are warranted to elucidate the identified ECG abnormalities and understand their electrophysiological substrates for LTA prediction in DCM.