MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification

ArXi:2605.24961v1 Announce Type: new Medical time series are central to healthcare, enabling continuous monitoring and ing timely clinical decisions. Despite recent progress, existing methods struggle to jointly model local-global dynamics and handle nonstationarities like baseline drift, while often failing to capture latent channel interactions. To address these challenges, we propose MedMamba, an end-to-end architecture that integrates state space models with domain-specific inductive biases.