Meredith: Learning Physiological Rhythms and Delayed Channel Interactions for Medical Time Series Classification

Accurate medical time series classification is important for identifying pathological conditions and supporting clinical diagnosis and disease management. Recent deep learning methods have achieved remarkable progress in this task, with their core designs largely centered on temporal representation learning and cross-channel modeling. However, prevailing strategies, such as generic multi-scale modeling for temporal representations and attention mechanisms or graph learning for cross-channel relationships, often lack explicit consideration of the intrinsic characteristics of physiological signals, potentially limiting the identification of diagnostically relevant patterns. To address this limitation, we draw upon physiological characteristics of medical time series to guide temporal representation learning and cross-channel modeling. First, we introduce Rhythm Mixer, which incorporates physiological rhythms into representation learning through learnable frequency decomposition and adaptive frequency-band aggregation, enabling medical-specific temporal representations beyond generic multi-scale modeling. Second, motivated by the temporal delays that can arise from the propagation and interaction of physiological activities, we propose the Delay-Aware Channel Enhancement Network (DACE Net), which accounts for different temporal delays to enhance each channel representation with delay-aware information from other channels. Building around these two core components, we develop Meredith, a physiology-informed framework for medical time series classification. Extensive experiments on five medical time series benchmarks demonstrate the strong and robust performance of Meredith against 10 competitive baselines under both Fixed Subject Split and Monte Carlo Cross-Validation protocols.