Key result
A deep neural network using Inception, GRU, and an attention mechanism classified nine arrhythmias from 12-lead ECGs with 0.928 accuracy, 0.901 sensitivity, and 0.984 specificity.
Why the study?
Accurate and efficient classification of arrhythmias according to an electrocardiogram is needed to address growing health issues caused by cardiovascular diseases.
Does a deep neural network based on Inception, GRU, and an attention mechanism accurately classify arrhythmias from 12-lead ECGs?
Does a deep neural network based on Inception, GRU, and an attention mechanism accurately classify arrhythmias from 12-lead ECGs?
A novel deep neural network using Inception, GRU, and an attention mechanism demonstrates high accuracy and stability in classifying nine arrhythmias from 12-lead ECGs.
Requires prospective validation before clinical use; leaves open generalizability across real-world ECG datasets.
Nowadays, a series of social problems caused by cardiovascular diseases are becoming increasingly serious. Accurate and efficient classification of arrhythmias according to an electrocardiogram is of positive significance for improving the health status of people all over the world. In this paper, a new neural network structure based on the most common 12-lead electrocardiograms was proposed to realize the classification of nine arrhythmias, which consists of Inception and GRU (Gated Recurrent Units) primarily. Moreover, a new attention mechanism is added to the model, which makes sense for data symmetry. The average F1 score obtained from three different test sets was over 0.886 and the highest was 0.919. The accuracy, sensitivity, and specificity obtained from the PhysioNet public database were 0.928, 0.901, and 0.984, respectively. As a whole, this deep neural network performed well in the multi-label classification of 12-lead ECG signals and showed better stability than other methods in the case of more test samples.
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Li et al. (2020) studied Arrhythmias. Deep neural network (Inception and GRU with attention mechanism) vs. Other methods was evaluated on Classification of nine arrhythmias (accuracy, sensitivity, specificity, F1 score). A deep neural network using Inception, GRU, and an attention mechanism classified nine arrhythmias from 12-lead ECGs with 0.928 accuracy, 0.901 sensitivity, and 0.984 specificity.
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