Key result
The DSE-ResNet model based on two-dimensional ECG achieved an average F1 score of 0.817 for classifying normal rhythm and 8 cardiac arrhythmias, outperforming other models in detecting atrial fibrillation and block abnormalities.
Why the study?
Most deep neural network models for automatic arrhythmia classification independently extract features from each lead in 12-lead ECGs, resulting in a lack of inter-lead features.
A two-dimensional ECG-based DSE-ResNet model demonstrates high accuracy in classifying cardiac arrhythmias, particularly atrial fibrillation and blocks, highlighting its potential as an auxiliary diagnostic tool.
Electrocardiogram (ECG) is mostly used for clinical diagnosis of cardiac arrhythmia due to its simplicity, non-invasiveness, and reliability. Recently, many models based on the Deep Neural Networks (DNNs) 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 9 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 F1 = 0.817 for classifying normal category and 8 cardiac arrhythmias. Meanwhile, compared with the state of the art model in CPSC2018, our model achieved the best F1 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 have 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 algorithm vs. State-of-the-art models in CPSC2018 was evaluated on Average F1 score for classifying normal rhythm and 8 cardiac arrhythmias. The DSE-ResNet model based on two-dimensional ECG achieved an average F1 score of 0.817 for classifying normal rhythm and 8 cardiac arrhythmias, outperforming other models in detecting atrial fibrillation and block abnormalities.
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