Summary of Ecgmamba: Towards Efficient Ecg Classification with Bissm, by Yupeng Qiang et al.
ECGMamba: Towards Efficient ECG Classification with BiSSM
by Yupeng Qiang, Xunde Dong, Xiuling Liu, Yang Yang, Yihai Fang, Jianhong Dou
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary ECG signal analysis plays a crucial role in diagnosing cardiovascular diseases, with transformer-based models making significant progress in classification tasks. However, these models are often inefficient during inference due to the secondary computational complexity of self-attention mechanisms, particularly when processing lengthy sequences. To address this issue, we propose ECGMamba, a novel model that employs a bidirectional state-space model (BiSSM) and incorporates time series modeling techniques through the Mamba-based block. This approach enhances classification performance while maintaining efficiency during inference. Experimental results on two publicly available ECG datasets demonstrate that ECGMamba achieves competitive performance, effectively balancing effectiveness and efficiency. Our study contributes to the field of ECG classification and provides a new research path for efficient and accurate signal analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Doctors use special signals called electrocardiograms (ECGs) to diagnose heart problems. Scientists have developed computer models that can help diagnose these problems by analyzing the ECG signals. However, these models can be slow when trying to analyze long sequences of data. To solve this problem, we created a new model called ECGMamba that uses a special technique to make analysis faster and more accurate. We tested our model on two big datasets of ECG signals and found that it worked really well, which could help doctors diagnose heart problems more quickly and accurately in the future. |
Keywords
* Artificial intelligence * Classification * Inference * Self attention * Time series * Transformer
