Title: PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation
Venue: NeurIPS 2025
Accepted: yes
Keywords: foundation model

biosignal

multi-modal analysis

deep learning
Abstract: Physiological signals are often corrupted by motion artifacts, baseline drift, and other low-SNR disturbances, posing 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, aimed at capturing multi-scale time-frequency features across 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 a 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 the analysis of diverse physiological signals, while the multi-modal design points to next-generation physiological signal processing with potential impacts on wearable health monitoring, clinical diagnostics, and broader biomedical applications. Code and
data are available at: github.com/ForeverBlue816/PhysioWave


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nthe authors proposed a new framework to develop foundational models for EMG and ECG, which can be extended to other biopotential. This framework manages multimodality, even when the signals present a different temporal scaling. They initially proposed a learnable \"wavelet\" trdecfomposition of the signal, which produces representations of the input data, and a frequency-guided masking used to train the model in a self-supervised way.\n\nSTRENGTHS AND WEAKNESSES:\nThe manuscript presents a novel framework to develop foundational models using self-supervised training. This approaches uses a new method for masking using the frequency content of the signal, and a noise generation. The algorithm is technically sound and outperform existing benchmarks in some ECG signals. Even though, there are not many benchmarks in the field.\n\nThe main concern for this manuscript is if the learnable filters for the wavelet indeed converge to a wavelet decomposition, or if this approach is only inspired in a wavelet decomposition. In addition, it will be nice to have some confidence intervals in the results obtained using your approach and other models.\n\nQUESTIONS:\nAnalysis of the Learned Wavelet\n\nAn analysis of the learned filter bank would be a valuable addition. Does the learned \"wavelet\" satisfy the mathematical properties of a true wavelet, such as the orthogonality, vanishing moments, compact support, smoothness? Furthermore, a visualization and discussion of the learned mother wavelet's shape would be highly informative.\n\nRegarding the frequency-guided masking, could you clarify if the noise was processed in the same manner as the input signal? Specifically, if the input signal was band-pass filtered, was the noise also filtered in the same frequency band? What will be the influence of this? If the noise is not band pass filter then it is easy to detect components that are not relevant impacting the generalization of the model.\n\nLIMITATIONS:\nThe manuscript should include a discussion about the limitation, some topics such as computational complexity, scarce datasets and its impact in generalization, the decomposition actually converges to a wavelet decomposition or is it only inspired on it?,", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents PhysioWave, a novel wavelet-based architecture for representing physiological signals, with a focus on EMG and ECG data. Its key contributions include a learnable wavelet decomposition module that captures multi-scale time-frequency features, a frequency-guided masking strategy to enhance self-supervised learning, and the development of large-scale pretrained models that achieve SOTA performance on various downstream tasks. Additionally, the authors introduce a unified multimodal framework that integrates pretrained EEG models, demonstrating improved results in applications such as emotion recognition and driving behavior analysis.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: The integration of adaptive wavelet decomposition with frequency-guided masking is a novel and practical approach to biosignal processing, enabling robust feature extraction across varying temporal and spectral scales. Empirical results across multiple benchmarks confirm consistent state-of-the-art performance, underscoring the robustness of the approach. Notably, the unified framework’s ability to fuse EMG, ECG, and EEG signals broadens its applicability and highlights its potential for multimodal analysis. The work is particularly relevant for healthcare applications, with reviewers emphasizing its utility in wearable monitoring, clinical diagnostics, and emerging directions in computational psychiatry.\n\nWeaknesses: The current validation is primarily focused on EMG and ECG modalities, with EEG support relying on external pretrained models and no exploration of other signals such as PPG. The computational complexity of the architecture may limit its feasibility for real-time or resource-constrained scenarios. Furthermore, the paper lacks an in-depth discussion of model interpretability and omits consideration of ethical safeguards, which is especially important for clinical and mental health applications. Although the authors suggest relevance to psychiatry, no psychiatric datasets or targeted evaluations are presented to substantiate these claims.\n\nQUESTIONS:\nCan the model be efficiently extended to other modalities, such as PPG or GSR?\n\nHow interpretable are the learned wavelet features, especially in terms of aligning with known physiological or cognitive markers?\n\nWhat is the model’s computational profile for real-time inference on edge devices?\n\nHow do you address ethical and privacy concerns, particularly in mental health and emotion recognition applications?\n\nLIMITATIONS:\nWhile the authors acknowledge some technical limitations, such as the focus on EMG and ECG and the absence of a universal biosignal model, they do not adequately address potential negative societal impacts. Specifically, the manuscript lacks discussion on model interpretability, data privacy risks, or ethical concerns related to clinical and mental health applications, such as bias in multimodal emotion recognition or unintended surveillance. To improve, the authors should explicitly consider ethical deployment scenarios, include interpretability strategies (e.g., aligning learned features with physiological markers), and propose safeguards for privacy and fairness in healthcare settings.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces PhysioWave, a multi-scale wavelet-Transformer framework designed to address the challenges of processing non-stationary, low-SNR physiological signals (e.g., EMG, ECG). The core innovation lies in combining adaptive wavelet decomposition with a frequency-guided masking (FgM) mechanism to capture multi-scale time-frequency features effectively. Key contributions include:\n\nAdaptive Wavelet Decomposition: A learnable wavelet selector dynamically combines wavelet bases to decompose signals into multi-resolution bands, capturing transient events (e.g., ECG QRS complexes) and low-frequency trends.\n\nFrequency-Guided Masking (FgM): A self-supervised strategy that masks high-energy spectral patches to force the model to learn context-dependent representations, improving generalization.\n\nLarge-Scale Pretrained Models: The first foundation models for EMG (trained on 823 GB) and ECG (182 GB), setting new SOTA on downstream tasks (e.g., 66.7% F1 on PTB-XL for arrhythmia detection).\n\nUnified Multi-Modal Framework: Fusing pretrained EMG/ECG models with existing EEG encoders, outperforming single-modality baselines (e.g., +7.3% accuracy on DEAP for emotion recognition).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nOriginality:\n\nThe integration of wavelet analysis with Transformer architectures is novel for biosignals, addressing non-stationarity better than fixed-window methods. First large-scale pretrained models for EMG/ECG fill a critical gap in the field, enabling downstream task generalization.\n\nMethodological Rigor:\n\nAblation studies validate the necessity of FgM and pretraining (e.g., removing FgM reduces F1 by 0.8% on EPN-612) .\nCross-dataset evaluations (6+ benchmarks) and multi-modal fusion demonstrate robustness.\n\nSignificance:\n\nEnables real-world applications in healthcare (wearable monitoring, clinical diagnostics) by improving signal reconstruction and classification .\nAddresses key challenges in biosignal processing (low SNR, inter-subject variability) via adaptive feature extraction .\n\nWeaknesses\n\nModality Limitation:\n\nFocus on EMG/ECG excludes EEG/PPG, limiting the framework’s universality .\n\n\nDataset Bias:\n\nDownstream datasets (e.g., DEAP) have limited demographic diversity, risking generalization to underrepresented groups .\n\nQUESTIONS:\nQuestion1: How would you adapt PhysioWave to EEG (low SNR, high channel count) or PPG (periodic signals)? Are there plans to pretrain models for these modalities?\n\nQuestion2: Have you tested PhysioWave on signals from diverse recording devices (e.g., consumer EMG bands vs. clinical ECG machines)? How to handle device-specific noise or sampling rate mismatches?\n\n\nQuestion3: The current fusion uses static weights. Will you explore dynamic alignment (e.g., attention-based modality weighting or time warping) to handle sampling rate disparities (e.g., EEG at 128 Hz vs. EMG at 2000 Hz)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nBriefly summarize the paper and its contributions. This is not the place to critique the paper; the authors should generally agree with a well-written summary. This is also not the place to paste the abstract—please provide the summary in your own understanding after reading.\nThe authors employed self-supervised pre-training in physiological signal processing, which helps build downstream multi-modal frameworks. To achieve this, they designed a wavelet decomposition module for tokenisation and reconstruction. Through extensive experiments, the authors validated the effectiveness of the designs.\n\nSTRENGTHS AND WEAKNESSES:\n[Strengths]\n1. The designed learnable wavelet decomposition supports adaptive filtering and downsampling/upsampling for different channels and scales, enhancing the details of signals;\n2. The wavelet feature reconstruction module enables a pre-training of the pipeline, and paves the way for downstream tasks.\n3. The experiments are clear and abundant, which makes the paper more convincing.\n[Weaknesses]\n1. Self-supervised pre-training is common in NLP and CV areas. It is also employed in the same area as mentioned in related works. Therefore, it is possible that as a technical contribution, it may lack sufficient innovation.\n2. The ablation study is relatively simple and fails to effectively verify proposed detailed sub-modules.\n\nQUESTIONS:\n1. Is it possible to verify the designed sub-modules? It is suggested to add the Adaptive Gating and Cross Scale CAFFN modules to the ablation studies.\n2. How to filter noise through soft gating and frequency-guided masking mechanism? Are there any quantitative or qualitative results?\n3. Can the multi-modal framework support dynamic number or combination of modals as inputs without re-training? What are the application scenarios of the method? Is it possible to provide the computing power or time consumption required for inference?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}