Hierarchical Topology-Aware Sparse Attention for Medical Time-Series Classification

Medical time series (MedTS) exhibit complementary temporal and inter-channel dependencies that are structured and non-uniform. Existing Transformer-based methods increasingly introduce structured representations and interactions, but generally do not adapt their interaction patterns to the distinct characteristics of temporal and inter-channel dependencies. We propose $\text{TopoAttn}$, a sparse attention framework that combines branch-specific interaction structures with hierarchical local-to-global communication. In the channel branch, direct interactions are determined by groups derived from training-set Fast Fourier Transform (FFT)-based spectral similarities, whereas the temporal branch restricts interactions to local temporal neighborhoods; learnable hubs coordinate information across groups. Mixture-of-Experts (MoE)-based temporal embedding and subject-guided enhancement further enrich the complementary branch representations. Under subject-independent five-fold evaluation on five EEG and ECG datasets, TopoAttn achieves the best result in 27 of 30 dataset-metric comparisons among the evaluated methods. Ablation and sensitivity analyses support the benefit of structured sparse interaction and branch-specific grouping beyond sparsity alone. TopoAttn also achieves the best result across all metrics on two human activity recognition datasets, supporting transfer beyond MedTS. The code will be released upon acceptance.