MedMamba: Recasting Mamba for Medical Time Series Classification
Medical time series, such as electrocardiograms (ECG) and electroencephalograms (EEG), exhibit complex temporal dynamics and structured cross-channel dependencies, posing fundamental challenges for automated analysis. Conventional convolutional and recurrent models struggle to capture long-range dependencies, while Transformer-based approaches incur quadratic complexity and often introduce redundant interactions that are misaligned with the intrinsic structure of physiological signals. To address these limitations, we propose MedMamba, a principle-driven multi-scale bidirectional state space architecture tailored for medical time series classification. Our design is guided by three key in...
| Authors | ZhengXiao He, Huayu Li, Xiwen Chen et al. |
|---|---|
| Primary category | eess.SP |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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