TF-MAMBANET

Z. Bao, Y. Benezeth, F. Yang, Y. Zhang, H. Wang, C. Li, "TF-MambaNet: A Temporal and Frequency Fused Bidirectional Mamba Architecture for PPG Foundation Model", IEEE ICASSP 2026 (pdf)

Photoplethysmography (PPG) is the most widely used non-invasive physiological signal monitoring technology. However, most existing deep learning models based on PPG are designed for specific tasks and lack cross-scenario generalization capability.

This paper proposes TF-MambaNet, a foundation model for PPG signals. The model is pre-trained on 50,000 hours of real-world PPG data collected from 100 users in real-world scenarios. It adopts a lightweight bidirectional Mamba architecture that integrates temporal and frequency domain information, combined with a novel multi-scale hybrid loss function.

This design enables the model to capture global trends, local patterns, and amplitude dynamics of blood volume pulse (BVP) signals. TF-MambaNet obtains competitive results while maintaining an extremely small parameter size (1.5M parameters), making it directly applicable as a feature extractor for personalized health monitoring.

Dataset and Code

You can download the dataset here (available very soon) and code here.

Reference

If you use this dataset, please cite this paper:

Zhanpeng Bao, Yannick Benezeth, Fan Yang, Yonglin Zhang, Haibin Wang, Chao Li
TF-MambaNet: A Temporal and Frequency Fused Bidirectional Mamba Architecture for PPG Foundation Model
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2026.