Curriculum Learning With Chaos-Aware Gating Mechanism for Infant Cardiorespiratory Status Prediction via Heart Rate Variability and Bedside Data

Predicting the cardiorespiratory status of extremely preterm infants using signals from bedside monitors is critical for timely intervention in neonatal intensive care units (NICUs), potentially improving long-term outcomes. However, directly adapting existing time series data analysis methods to this task is challenged by the scarcity of neonatal data and the high noise levels inherent to physiological signals in this population. To address these challenges, we propose a novel hybrid neural network that integrates C urriculum L earning and a C haos-aware G ating mechanism, called CLCG. Given the long multivariate physiological sequences, we first apply a sliding-window strategy to segment the data into overlapping windows. For each window, a shared Convolutional Neural Network (CNN) fuses multi-modal signals and extracts localized representations. To increase data diversity and enhance generalization capability, we introduce a curriculum learning-based data augmentation scheme that progressively reduces the number of windows during training. This encourages the model to learn discriminative representations by exposing it to increasingly challenging scenarios with less temporal context. To further mitigate the impact of signal noise and improve temporal modeling, we extract handcrafted features, the Lyapunov exponent, from the heart rate variability (HRV) parameters. These features are incorporated into a novel chaos-aware gate mechanism within the Long Short-Term Memory (LSTM) network. This mechanism guides the gating process based on nonlinear dynamical properties, and suppresses noise-related fluctuations. Finally, a Multi-Layer Perceptron (MLP) predicts the respiratory and hypotensive status over the next five hours. We evaluate our model on two datasets: an in-house cohort of 55 extremely preterm infants from the Monash Children's Hospital, and the publicly available MC-MED dataset from emergency department patients. Our approach outperforms 6 other state-of-the-art time-series models for both datasets, achieving significantly higher F1 scores ( p <0.05). Ablation studies confirm the effectiveness of both curriculum learning and chaos-aware gating mechanism, highlighting the potential of our framework for clinical prediction in high-noise, low-data neonatal care settings.