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

Jiale Gao, Yihao Yao, Tao Liang, Wentao Xiang, Wei Wang, Xiaofeng Liu, Angelo Cangelosi, Songsheng Zhu, Jianqing Li, Jie Li

Abstract: 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 (MCI)]. This challenge is rooted in three key issues: the complex multiscale nature of ECG signals, high interindividual physiological variability, and the need for computationally efficient temporal modeling for Internet of Things (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 multiscale features analogous to heart rate variability (HRV) analysis; an innovative nonlocal channel convolutional attention (NLCCA) mechanism mitigates interindividual 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 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 (SOTA) 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

External IDs:doi:10.1109/jiot.2026.3651586