PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation

TL;DR

PhysioWave introduces a multi-scale wavelet-transformer architecture for physiological signals, effectively capturing non-stationary features and improving analysis across multiple modalities, with pretrained models for EMG, ECG, and EEG.

Contribution

The paper presents a novel wavelet-based multi-scale transformer architecture and pretrained models for EMG, ECG, and EEG, advancing physiological signal analysis and multi-modal integration.

Findings

Outperforms existing methods on physiological signal tasks.

Pretrained models achieve superior performance in downstream applications.

Effective multi-modal fusion enhances robustness to noise and variability.

Abstract

Physiological signals are often corrupted by motion artifacts, baseline drift, and other low-SNR disturbances, which pose significant challenges for analysis. Additionally, these signals exhibit strong non-stationarity, with sharp peaks and abrupt changes that evolve continuously, making them difficult to represent using traditional time-domain or filtering methods. To address these issues, a novel wavelet-based approach for physiological signal analysis is presented, aiming to capture multi-scale time-frequency features in various physiological signals. Leveraging this technique, two large-scale pretrained models specific to EMG and ECG are introduced for the first time, achieving superior performance and setting new baselines in downstream tasks. Additionally, a unified multi-modal framework is constructed by integrating pretrained EEG model, where each modality is guided through its dedicated branch and fused via learnable weighted fusion. This design effectively addresses challenges such as low signal-to-noise ratio, high inter-subject variability, and device mismatch, outperforming existing methods on multi-modal tasks. The proposed wavelet-based architecture lays a solid foundation for analysis of diverse physiological signals, while the multi-modal design points to next-generation physiological signal processing with potential impact on wearable health monitoring, clinical diagnostics, and broader biomedical applications. Code and data are available at: github.com/ForeverBlue816/PhysioWave