NeurOCNN: A Neural-Operator-Based Model for Physiological Time Series

Neural operators have become a central tool in scientific machine learning for learning discretization-consistent solution operators, achieving strong results on partial differential equation (PDE) benchmarks. Physiological time series, however, are highly nonstationary and dominated by localized transient events, properties that can challenge both PDE-oriented neural operators and conventional deep models. We propose NeurOCNN, a neural-operator-based model for physiological signals that learns a function-to-label mapping while exhibiting discretization invariance. NeurOCNN integrates continuous-time, spline-parameterized convolutions with Fourier projection pooling and an attention-based task head, thereby enabling robust inference under sampling-rate shifts. Empirically, NeurOCNN outperforms standard neural-operator baselines, achieves performance comparable to state-of-the-art methods, and maintains stable accuracy under zero-shot evaluation across multiple previously unseen sampling rates. Code is available at: https://github.com/dcoder444/NeurOCNN.git