Key result
The DSE-ResNet model achieved an average F1 score of 0.817 for classifying normal rhythm and 8 cardiac arrhythmias, with the highest performance in detecting atrial fibrillation (F1 0.944) and blocks (F1 0.913).
Why the study?
Most deep neural network models for automatic arrhythmia classification extract features from each 12-lead ECG lead independently, lacking inter-lead feature extraction.
Does a two-dimensional ECG-based DSE-ResNet model improve the automatic classification of cardiac arrhythmias compared to existing models?
Does a two-dimensional ECG-based DSE-ResNet model improve the automatic classification of cardiac arrhythmias compared to existing models?
The DSE-ResNet model using two-dimensional ECG processing achieves high accuracy in classifying cardiac arrhythmias, particularly atrial fibrillation and bundle branch blocks, demonstrating potential as a clinical auxiliary tool.
May enhance 12-lead ECG arrhythmia classification; leaves open prospective clinical validation before practice change.
Electrocardiogram (ECG) is mostly used for the clinical diagnosis of cardiac arrhythmia due to its simplicity, non-invasiveness, and reliability. Recently, many models based on the deep neural networks have been applied to the automatic classification of cardiac arrhythmia with great success. However, most models independently extract the internal features of each lead in the 12-lead ECG during the training phase, resulting in a lack of inter-lead features. Here, we propose a general model based on the two-dimensional ECG and ResNet with detached squeeze-and-excitation modules (DSE-ResNet) to realize the automatic classification of normal rhythm and 8 cardiac arrhythmias. The original 12-lead ECG is spliced into a two-dimensional plane like a grayscale picture. DSE-ResNet is used to simultaneously extract the internal and inter-lead features of the two-dimensional ECG. Furthermore, an orthogonal experiment method is used to optimize the hyper-parameters of DSE-ResNet and a multi-model voting strategy is used to improve classification performance. Experimental results based on the test set of China Physiological Signal Challenge 2018 (CPSC2018) show that our model has average [Formula: see text] for classifying normal rhythm and 8 cardiac arrhythmias. Meanwhile, compared with the state-of-art model in CPSC2018, our model achieved the best [Formula: see text] in 2 sub-abnormal types. This shows that the model based on the two-dimensional ECG and DSE-ResNet has advantage in detecting some cardiac arrhythmias and has the potential to be used as an auxiliary tool to help doctors perform cardiac arrhythmias analysis.
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Li et al. (2022) studied Cardiac arrhythmia (n=9,831). DSE-ResNet vs. Other state-of-the-art models was evaluated on Average F1 score for classifying normal rhythm and 8 cardiac arrhythmias. The DSE-ResNet model achieved an average F1 score of 0.817 for classifying normal rhythm and 8 cardiac arrhythmias, with the highest performance in detecting atrial fibrillation (F1 0.944) and blocks (F1 0.913).
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