Key result
STCA-Net with SAI matches state-of-the-art accuracy for irregular physiological signals while remaining lightweight.
Why the study?
Existing approaches to handling irregular physiological signal data are computationally demanding and fail to capture inherent periodic patterns, limiting accurate real-time monitoring.
The proposed STCA-Net provides a lightweight and effective approach for classifying irregular physiological signals, which may improve automated analysis for wearable devices.
The rapid development of wearable devices and remote monitoring technologies has accelerated the demand for automated analysis of physiological signals. In this process, inevitable signal loss leads to irregular data, which poses challenges to accurate real-time monitoring. However, existing approaches to handling irregular medical data mainly center on electronic health records (EHR). These records are discrete, non-periodic and unsuitable for direct use in physiological signal analysis. Although some techniques have been developed to model irregular data, they often demand high computational resources and fail to capture inherent periodic patterns. To address these limitations, we propose Structural-to-Temporal Cross-Attention (STCA) to fuse structural and temporal information. We also introduce a Self-Adaptive Interpolation (SAI) strategy that explicitly capitalizes on the periodicity of such signals. Extensive experiments demonstrate that our STCA-Net combined with SAI achieves competitive performance comparing with state-of-the-art methods while remaining lightweight.
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Wu et al. (2026) studied Irregular physiological signals. STCA-Net combined with Self-Adaptive Interpolation (SAI) vs. State-of-the-art methods was evaluated on Classification performance. STCA-Net combined with Self-Adaptive Interpolation achieved competitive performance compared with state-of-the-art methods for classifying irregular physiological signals while remaining lightweight.
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