Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.10098 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929386706436096 |
|---|---|
| author | Qiang, Yupeng Dong, Xunde Liu, Xiuling Yang, Yang Fang, Yihai Dou, Jianhong |
| author_facet | Qiang, Yupeng Dong, Xunde Liu, Xiuling Yang, Yang Fang, Yihai Dou, Jianhong |
| contents | Electrocardiogram (ECG) signal analysis represents a pivotal technique in the diagnosis of cardiovascular diseases. Although transformer-based models have made significant progress in ECG classification, they exhibit inefficiencies in the inference phase. The issue is primarily attributable to the secondary computational complexity of Transformer's self-attention mechanism. particularly when processing lengthy sequences. To address this issue, we propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. ECGMamba is based on the innovative Mamba-based block, which incorporates a range of time series modeling techniques to enhance performance while maintaining the efficiency of inference. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification, achieving competitive performance. This study not only contributes to the body of knowledge in the field of ECG classification but also provides a new research path for efficient and accurate ECG signal analysis. This is of guiding significance for the development of diagnostic models for cardiovascular diseases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10098 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ECGMamba: Towards Efficient ECG Classification with BiSSM Qiang, Yupeng Dong, Xunde Liu, Xiuling Yang, Yang Fang, Yihai Dou, Jianhong Machine Learning Artificial Intelligence Electrocardiogram (ECG) signal analysis represents a pivotal technique in the diagnosis of cardiovascular diseases. Although transformer-based models have made significant progress in ECG classification, they exhibit inefficiencies in the inference phase. The issue is primarily attributable to the secondary computational complexity of Transformer's self-attention mechanism. particularly when processing lengthy sequences. To address this issue, we propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. ECGMamba is based on the innovative Mamba-based block, which incorporates a range of time series modeling techniques to enhance performance while maintaining the efficiency of inference. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification, achieving competitive performance. This study not only contributes to the body of knowledge in the field of ECG classification but also provides a new research path for efficient and accurate ECG signal analysis. This is of guiding significance for the development of diagnostic models for cardiovascular diseases. |
| title | ECGMamba: Towards Efficient ECG Classification with BiSSM |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.10098 |