<p>Transformer has shown great potential in modeling global dependencies in sequential data, research on Transformer-based variant architectures for medical time series analysis has attracted growing attention. However, challenges remain in capturing complex local patterns and long-term dependencies in medical time series due to their high-dimensional nonlinearities and temporal correlations. To address this, we propose a Transformer model DMKformer which incorporates a new feature fusion strategy named 2R-Attention. Specifically, on the basis of the original Transformer, we adopt the STAR and the self-attention structure to enhance the ability of the attention module to capture and utilize the information of long-term dependencies in the global signal. Meanwhile, a one-dimensional DWC module is introduced to improve local information capture and reduce overfitting. Additionally, we employ Mamba to optimize FFN for long sequences and utilize a KAN network to strengthen classification capabilities. We conduct extensive experiments on three publicly available datasets under both subject-independent and subject-dependent settings. The experimental results demonstrate that our method outperforms 11 benchmark models, achieving an average F1 score improvement of 2%, validating its effectiveness.</p>