Key result
Residual network diagnoses AF from ECG data with ~100% accuracy, outperforming Alex network models.
Why the study?
The global age-adjusted mortality rate related to atrial fibrillation increased rapidly over the last four decades, motivating improved diagnostic methods using electrocardiogram data.
Do residual networks provide better accuracy than Alex networks for diagnosing atrial fibrillation from ECG data?
Observational (n=20,000)
No
Do residual networks provide better accuracy than Alex networks for diagnosing atrial fibrillation from ECG data?
Residual networks provide highly accurate diagnosis of atrial fibrillation from ECG data with fewer parameters than Alex networks.
Answers are grounded in this paper and cite their sources.
Residual networks may enable efficient AF detection on ECG; hypothesis-generating and requires prospective validation before clinical use.
BACKGROUND: The global age-adjusted mortality rate related to atrial fibrillation (AF) registered a rapid growth in the last four decades, i.e., from 0.8 to 1.6 and 0.9 to 1.7 per 100,000 for men and women during 1990-2010, respectively. In this context, this study uses convolutional neural networks for classifying (diagnosing) AF, employing electrocardiogram data in a general hospital. METHODS: Data came from Anam Hospital in Seoul, Korea, with 20,000 unique patients (10,000 normal sinus rhythm and 10,000 AF). 30 convolutional neural networks were applied and compared for the diagnosis of the normal sinus rhythm vs. AF condition: 6 Alex networks with 5 convolutional layers, 3 fully connected layers and the number of kernels changing from 3 to 256; and 24 residual networks with the number of residuals blocks (or kernels) varying from 8 to 2 (or 64 to 2). RESULTS: In terms of the accuracy, the best Alex network was one with 24 initial kernels (i.e., kernels in the first layer), 5,268,818 parameters and the training time of 89 s (0.997), while the best residual network was one with 6 residual blocks, 32 initial kernels, 248,418 parameters and the training time of 253 s (0.999). In general, the performance of the residual network improved as the number of its residual blocks (its depth) increased. CONCLUSION: For AF diagnosis, the residual network might be a good model with higher accuracy and fewer parameters than its Alex-network counterparts.
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Lee et al. (2019) conducted an observational in Atrial fibrillation (n=20,000). Residual network (Convolutional neural network) vs. Alex network was evaluated on Accuracy of atrial fibrillation classification. A residual network with 6 residual blocks achieved 99.9% accuracy in diagnosing atrial fibrillation from electrocardiogram data, outperforming the best Alex network model (99.7% accuracy).
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