MaKAN-Mixer: Channel Interaction-Based Mamba Method for rPPG Extraction

作者：Hengrui Zhang, Feiyang Liao, Gang Yuan, Haoyang Jin, Biao Xie, Xu Cao, Mingcui Fu, Jian Zheng · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2025 · DOI：10.1109/jbhi.2025.3532488 · 被引用次数：3 · 研究领域：Natural Language Processing Techniques

Remote photoplethysmography (rPPG) achieves non-contact heart rate monitoring by detecting subtle skin color variations in facial videos, offering significant potential in healthcare, fitness, and security applications.However, accurately extracting rPPG signals in complex environments-especially under variable lighting and motion artifacts-remains challenging. The main difficulties are capturing spatio-temporal dynamics and modeling long-term dependencies across channels. To address these limitations, we propose MaKAN-Mixer, a novel end-to-end network designed to enhance the robustness and accuracy of rPPG signal extraction. First, MaKAN-Mixer integrates a Hybrid of Eulerian Video Magnification and Temporal Shift Module Amplification (HETA) to amplify subtle physiological signals and enhance temporal information without relying on explicit region-of-interest (ROI) selection. Additionally, we propose the Mamba-KAN Fusion Module (MKFM), which leverages Mamba's ability to efficiently model long-term dependencies in temporal sequences. By incorporating the Kolmogorov-Arnold Network (KAN) for effective channel mixing, MKFM ensures the comprehensive fusion of relevant spatio-temporal features across different channels. Finally, we employ a KAN Feedforward Neural Network (KFN) to capture complex, nonlinear, and periodic physiological patterns, improving heart rate estimation. Extensive experiments conducted on four benchmark datasets demonstrate that MaKAN-Mixer achieves superior p...