Non-contact heart rate measurement methods based on remote PhotoPlethysmoGraphy （rPPG） technology face two main challenges： the accurate selection of facial Regions of Interest （RoI）， and the precise interpretation of periodic patterns in rPPG from long video sequences. To address the inaccurate localization of facial RoI in the existing works and the neglect of temporal lag among different signal sources， an end-to-end rPPG signal extraction model， named RppgMamba， was proposed for non-contact heart rate measurement. In the model， the hierarchical spatial-temporal Mamba module with cross-scan mechanism was designed to capture spatial perception relationships of weak signals from multi-scale spatial-temporal receptive fields， so as to localize facial RoI adaptively， thereby avoiding interference from background and motion noise. And the frequency-domain refinement module was designed to further learn the quasi-periodic patterns of rPPG signals in the frequency domain， which enhanced output signal quality and corrected phase shifts of the output signals combining with phase compensator， thereby reducing the impact of inherent temporal lag. Experimental results demonstrate that the proposed model achieves the best performance on three public datasets， confirming its high accuracy and applicability in heart rate measurement.