The attention-based time-incremental convolutional neural network (ATI-CNN) achieved an overall classification accuracy of 81.2%, an average increase of 7.7% compared to a classical 16-layer CNN.
Does ATI-CNN improve classification accuracy for multi-class arrhythmia detection from varied-length ECGs compared to classical CNN?
ATI-CNN provides a computationally efficient and more accurate deep learning approach for detecting arrhythmias from varied-length ECGs compared to traditional CNN models.
Automatic arrhythmia detection from Electrocardiogram (ECG) plays an important role in early prevention and diagnosis of cardiovascular diseases. Convolutional neural network (CNN) is a simpler, more noise-immune solution than traditional methods in multi-class arrhythmia classification. However, suffering from lack of consideration for temporal feature of ECG signal, CNN couldn’t accept varied-length ECG signal and had limited performance in detecting paroxysmal arrhythmias. To address these issues, we proposed attention-based time-incremental convolutional neural network (ATI-CNN), a deep neural network model achieving both spatial and temporal fusion of information from ECG signals by integrating CNN, recurrent cells and attention module. Comparing to CNN model, this model features flexible input length, halved parameter amount as well as more than 90% computation reduction in real-time processing. The experiment result shows that, ATI-CNN reached an overall classification accuracy of 81.2%. In comparison with a classical 16-layer CNN named VGGNet, ATI-CNN achieved accuracy increases of 7.7% in average and up to 26.8% in detecting paroxysmal arrhythmias. Combining all these excellent features, ATI-CNN offered an exemplification for all kinds of varied-length signal processing problems.
Yao et al. (2019) studied Multi-class Arrhythmia. Attention-based time-incremental convolutional neural network (ATI-CNN) vs. Classical 16-layer CNN (VGGNet) was evaluated on Overall classification accuracy. The attention-based time-incremental convolutional neural network (ATI-CNN) achieved an overall classification accuracy of 81.2%, an average increase of 7.7% compared to a classical 16-layer CNN.