Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network

• Published in: Information fusion 2020-01, Vol.53, p.174-182
• Main Authors: Yao, Qihang, Wang, Ruxin, Fan, Xiaomao, Liu, Jikui, Li, Ye
• Format: Article
• Language: English
• Subjects: Arrhythmia detection; Attention module; Convolutional neural network; Recurrent cells; Spatial temporal fusion
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2019.06.024

•Effectiveness of spatial and temporal fusion in signal analysis was demonstrated.•Accuracy improvement of 81.2% in detection of 9 arrhythmias from electrocardiogram.•Up to 26.8% accuracy increase in detection of paroxysmal arrhythmias.•Halved memory consumption and more than 90% computation overhead reduction.•Enabled localization of irregular signal segments and increased interpretability. 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.