A Transferable Hybrid Convolutional-Mamba Network for Cross-Population Emotion Recognition From Wearable ECG

Leveraging electrocardiogram (ECG) signals for emotion recognition represents a core challenge in affective computing, particularly in achieving robustness across diverse demographic groups (such as older adults with mild cognitive impairment). This challenge is rooted in three key issues: the complex multi-scale nature of ECG signals, high inter-individual physiological variability, and the need for computationally efficient temporal modeling for IoT applications. To address these issues systematically, this study proposes HCMNet, a novel, physiologically-inspired hybrid Convolutional-Mamba network. HCMNet’s architecture is problem-driven: a hierarchical scale-aware convolutional module captures multi-scale features analogous to HRV analysis; an innovative Non-Local Channel Convolutional Attention (NLCCA) mechanism mitigates inter-individual variability by learning to reshape the feature space; and a Mamba2-based Bidirectional State-Space Model (BiSSM) efficiently models temporal dynamics with linear complexity. Additionally, we validated the model on a self-built Wearable ECG emotion dataset comprising healthy elderly individuals and patients with mild cognitive impairment (MCI), as well as on public datasets WESAD and DREAMER. Experimental results demonstrate that our proposed HCMNet, through its synergistic hybrid architecture, effectively extracts robust emotional features. It not only achieves state-of-the-art performance on public benchmarks but also exhibits strong robustness for special populations. Furthermore, our in-depth adaptation analysis reveals that while a “one-model-fits-all” approach is infeasible for unseen subjects, HCMNet excels as a robust transferable base model that can be rapidly personalized, offering a practical paradigm for accurate and adaptable emotion recognition in real-world IoT settings. The source code is available at https://github.com/INSOCE/HCMNet.