Key result
One-dimensional deep learning models (LiteVGG-11) differentiated atrial fibrillation from atrial flutter with 77.91% accuracy, outperforming two-dimensional image-based models.
Why the study?
Atrial fibrillation and atrial flutter pose significant clinical risks, but automated detection systems frequently misdiagnose them due to symptom similarities.
Can deep learning models accurately differentiate atrial fibrillation from atrial flutter using standard 12-lead ECGs?
Can deep learning models accurately differentiate atrial fibrillation from atrial flutter using standard 12-lead ECGs?
Absolute Event Rate: 77.91% vs 75.2%
Deep learning models can differentiate atrial fibrillation from atrial flutter on 12-lead ECGs, with 1D signal-based models slightly outperforming 2D image-based models, though external validation performance remains a challenge.
Modest DL performance (~78% acc) for AFib vs AFlut differentiation cautions against over-reliance on automation; leaves open need for improved models and validation.
Atrial Fibrillation (AFib) and Atrial Flutter (AFlut) are prevalent irregular heart rhythms that poses significant risks, particularly for the elderly. While automated detection systems show promise, misdiagnoses are common due to symptom similarities. This study investigates the differentiation of AFib from AFlut using standard 12-lead ECGs from the PhysioNet CinC Challenge 2021 (CinC2021) databases, along with data from a private database. We employed both one dimensional-based (1D) and image-based (2D) Deep Learning models, comparing different 1D and 2D Convolutional Neural Network (CNN) architectures for classification. For 1D models, LiteVGG-11 demonstrated the highest performed, achieving an accuracy (Acc) of 77.91 (±1.73%), area under the receiver operating characteristic curve (AUROC) of 87.17 (±1.29%), F1 score of 76.59 (±1.90%), specificity (Spe) of 71.69 (±4.73%), and sensitivity (Se) of 86.53 (±5.33%). On the other hand, for 2D models the EfficientNet-B2 outperformed other architectures, with an Acc of 75.20 (±3.38%), AUROC of 85.50 (±1.14%), F1 of 71.59 (±3.66%), Spe of 74.76 (±13.85%) and Se of 75.74 (±13.85%). Our findings indicate that distinguishing between AFib and AFlut is non-trivial, with 1D signals exhibiting superior performance compared to their 2D counterparts. Furthermore, it’s noteworthy that the performance of our models on the CinC2021 databases was considerably lower than on our private dataset.
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Ribeiro et al. (2023) studied Atrial Fibrillation and Atrial Flutter (n=23,139). 1D Convolutional Neural Networks (LiteVGG-11) vs. 2D Convolutional Neural Networks (EfficientNet-B2) was evaluated on Accuracy in differentiating Atrial Fibrillation from Atrial Flutter. One-dimensional deep learning models (LiteVGG-11) differentiated atrial fibrillation from atrial flutter with 77.91% accuracy, outperforming two-dimensional image-based models.
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