WaveMamba-Net: Dual-frequency adaptive wavelet state-space network for real-time pulse signal classification in wearable health monitoring.

Continuous cardiovascular monitoring via wearable devices is critical for early disease detection, yet existing pulse signal analysis methods struggle to achieve both high accuracy and real-time performance under noisy, imbalanced conditions. We propose WaveMamba-Net, a deep learning framework integrating wavelet-based multi-scale decomposition with state-space modeling for patient status classification from photoplethysmography signals. Discrete wavelet transform decomposes pulse signals into frequency-specific components, processed by dual-view Wavelet Temporal and Spatial Sliding Enhance Modules capturing interperiod rhythm and intraperiod morphological features. Selective state-space Mamba blocks enable efficient long-range dependency modeling, while Multi-Wavelet Condition Convolution adaptively integrates decomposition levels via input-dependent kernel modulation. Evaluated on Medical Information Mart for Intensive Care III (MIMIC-III) Waveform Database and PPG-DaLiA Dataset, WaveMamba-Net achieves 91.84% accuracy and 87.52% Macro-F1 for arrhythmia classification, and 88.26% accuracy with 86.91% Macro-F1 for stress assessment, outperforming nine baselines with 12 ms inference latency suitable for edge deployment.