MultiECGNet: Evaluation of a Deep-Learning Ensemble Method for Image-Based 12-Lead Electrocardiographic Detection of Atrial Fibrillation.

Background: Artificial intelligence holds significant promise for analysis and interpretation of electrocardiograms (ECGs) in cardiovascular disease. However, many ECGs are still stored as paper records or static images, necessitating deep-learning models that can operate on image-based ECG formats. This study aims to evaluate the performance of an ensemble classifier, MultiECGNet, using multi-format ECG images for the diagnosis of atrial fibrillation (AF). Methods: An ensemble classifier was developed using 4 models derived by truncating the pretrained EfficientNet B3 model at different feature extraction layers. Transfer learning was employed to train the ensemble on the publicly available PTB-XL dataset for AF detection. External validation was performed using the Chinese Physiological Society Signal Challenge dataset. ECG samples were converted into 2 image formats (2 x 6 and 4 x 3), and performance was evaluated across same-format, cross-format, and mixed-format classification tasks. Results: The classifier detected AF in the validation dataset with an accuracy of 0.95 with an F1-score of 0.87, comparable to a signal-based model (F1-score: 0.87 vs 0.83) and better than a single EfficientNet-B3 model (F1-score: 0.87 vs 0.71). Training on one ECG format and testing on a different format resulted in reduced performance (F1-score: 0.66-0.71). However, training on a dataset containing a mix of both formats closely matched the performance of the original same-format training (F1-score: 0.86). Conclusions: The proposed image-based ensemble classifier demonstrated comparable performance to a strong signal-based model for AF detection. Although cross-format generalization posed a challenge, incorporating multiple ECG formats during training mitigated this limitation and improved model robustness.