| Paper ID | BIO-2.6 |
| Paper Title |
ARRHYTHMIA CLASSIFICATION WITH HEARTBEAT-AWARE TRANSFORMER |
| Authors |
Bin Wang, Chang Liu, Chuanyan Hu, Xudong Liu, Jun Cao, Lepu Medical Technology, China |
| Session | BIO-2: Biomedical Signal Processing: Detection and Estimation |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO] Biomedical signal processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Electrocardiography (ECG) is a conventional method in arrhythmia diagnosis. In this paper, we proposed a novel neural network model which treats typical heartbeat classification task as ‘Translation’ problem. By introducing Transformer structure into model, and adding heartbeat-aware attention mechanism to enhance the alignment between encoded sequence and decoded sequence, after trained with ECG database, (which are collected from 200k patients in over 2000 hospitals for more than 10 years), the validation result of independent test dataset shows that this new heartbeat-aware Transformer model can outperform classic Transformer and other sequence to sequence methods. Finally, we show that the visualization of encoder-decoder attention weights provides more interpretable information about how a Transformer make a diagnosis based on raw ECG signals, which has guiding significance in clinical diagnosis. |
