NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals
State space models (SSMs) achieve linear-time complexity but struggle with multi-channel physiological signals due to three limitations: fixed kernels cannot capture multi-scale temporal dynamics (motor preparation over hundreds of milliseconds vs. execution transients in tens of milliseconds), Markovian state updates restrict global context for periodic oscillations, and channel-independent processing ignores spatial electrode topology. We introduce NAKUL, extending SSMs for medical signal analysis through three contributions: (1) Dynamic Kernel Generation-parallel SSM branches with varying kernel sizes (3, 5, 7, 11 timesteps) are weighted by a meta-network that analyzes input statistics...
| Authors | Badri N. Patro, Vijay S. Agneeswaran |
|---|---|
| Primary category | eess.SP |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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