Context-Conditioned Tokenization for Medical Time Series Classification

Local discriminative patterns in medical time series often depend on neighboring waveforms and the overall physiological state, whereas conventional token formation compresses local observations before contextual modeling, potentially weakening fine-grained cues that can only be identified with sufficient context. To address this issue, we propose ReConTok, a context-conditioned token formation framework for medical time series classification. Its core module, Context-guided Token Gating and Fusion (CoTGF), jointly exploits the current local representation, neighborhood context, and global summary to conditionally gate and fuse fine-grained features. The same mechanism is applied to both temporal and channel tokens while leaving the subsequent Transformer interaction unchanged. Across five EEG/ECG datasets, ReConTok achieves the best F1 on four datasets and shows consistent advantages in Recall. Controlled tokenizer comparisons, short-versus-long window experiments, and robustness analyses further validate its effectiveness. These results demonstrate that directly incorporating context into token formation yields more discriminative and robust representations for medical time series. blueCode is available in the supplementary material.