PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation
Authors: Yanlong Chen
Mattia Orlandi
Pierangelo Maria Rapa
Simone Benatti
Luca Benini
Yawei Li
Venue: NeurIPS 2025 poster ()
Keywords: foundation model
biosignal
multi-modal analysis
deep learning
Primary area: machine_learning_for_sciences

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

Physio Wave: A Multi-Scale Wavelet-Transforme for Physiological Signal Representation
Yanlong Chen'
Mattia Orlandi
Pierangelo Maria Rapa² Yawei Li
Simone Benatti
Luca Benini
IIS, ETH Zurich
DEI, University of Bologna
DIEF, University of Modena and Reggio Emilia
yanlchen@student.c ethz. ch
(mattia. Mlattia.orlandi. borrangelomari..rape... simone. benatti)@unibo... it lbenini@iis ethz. ch, 1i yawei. ai dai@@mmalll.. com
Abstract
Physiological signals are often corrupted by motion artifacts, baseline drift, and other low-SNR disturbances, which pose significant challenges for analysis. Addi- tionally, these signals exhibit strong aa-staaiinnaity, with sharp peaks and abrupt changes that evolve continuously, making them difficult to represent using tradi- tional 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. Leverag- ing 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, 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-ne -o-ise ratio, high inter-subject variability, and device mismatch, outperforming existing methods tasks. The proposed wavelet- based architecture lays a solid foundation for analy- sis of diverse physiological signals, while the multi-moda design points next- generation physiological signal processing with potential impact on wearable health monitoring, clinical diagnostics, and broader biomedical applications. O Code and data are available at: github. cot.:/orreeerrleeeel//////lyooo
Introduction
Physiological signals such as electroencephalograply (EEG), electromyography (EMG), and elec- trocardiography (ECG) are essential for health monitoring, clinical diagnosis, and brain-computer interfacing []] Although large-scals foundation models for generic time-series data have recently shown remarkable success [2] 31 pretrained networks for biosignals remain scarce. While several EEG-specific encoders have been developed, such as LaBraM, which enables cross-dataset learning by segmenting signals into channel patches and training vector-quantized neural spectrum tok- enizer (4, and EEGPT, a pretrained Transformer designed for universal EEG feature extraction that combines masked reconstruction with spatio-temporal representation alignment to mitigate issues of low SNR and inter-subject variability [ similar models for other biosignals are still lacking.
Cooresppndingg author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Biosignals differ fundamentally from images or language: they evolve continuously, exhibit strong non-stationarity, and are often corrupted by motion artifacts, baseline drift, and other low-SNR distur- bances [6] These challenges, compounded by high inter-subject variability, hinder the application of standard deep learning techniques 77 As result, there is an urgent need for modeling frameworks that can naturally accommodate the multi-scale, noisy, and heterogeneous nature of physiological time-series.
Traditional time-domain methods often overlook the rich spectral content of biosignals g while trequency-domall domain methods like fixed-window Fourier transforms assume stationarity, losing tempo- ral resolution in rapidly changing signals [9]. In contrast, wavelet- based time-freuu frequency methods can adaptively capture both fast transients and slower dynamics, making them ideal for nonstation- ary physiological data [T0] M.. However, modalities such as optical biosensing and EEG differ significantly in terms of dimension, sampling rate, and resolution, requiring modality-specifie pre- processing. Existing segmentation and tokenization pipelines are not cross-modal, making them unsuitable for handling diverse signal types [12]. To address these issues, we propose a learnable wavelet decomposition pipeline that automatically selects and fuses multi-resolution filters based on signal content. Instead of relying on hand-engineered wavelet families, our method uses an Adaptive Wavelet Selector assign optimal wavelet kernels for each channel, then aggregates approximate and detail components across multiple scales. This approach effectively captures both transient spikes (e.g., EMG bursts or ECG QRS complexes) and sustained wwwfreguency fluctuations, providing robust front-end for downstream tasks [3 14].
Although self-super learning has demonstrated success in time-series data, methods inspired by natural language processing often discard contiguous tokens in the time domain 15.. Unlike words, raw biosignal segments do not neatly correspond to meaningful units, and randomly discarding them may remove key events or mask redundant portions, ultimately limiting model performance [16]. To overcome this, we introduce Frequency-guided Masking (FgM), a mechanism that selectively occludes the most informative segments. Inspired by SpecAugment's frequency masking and the masked acoustic modeling in HuBERT [17 18]. FgM measures the spectral energy of each patch to identify high-infon high-information regions. By masking high-energy patches more frequently, FgM forces the model to infer crucial details from context, thereby tightening the information bottleneck. A mix of energy-driven and random masking ensures training variability, preventing overfitting to fixed patterns and improving task generalization.
The wavelet-based decomposition and frequency-guideds masking (FgM) work in tandem: adaptive wavelet filters separate the signal into multi-resolution bands, while FgM selectively masks bands with higher spectral energy, making them more likely to be occluded. This strategy of multiscale decomposition combined with energy- foccuse masking encourages the model to capture physiologi- cally relevant patterns and reduce redundancy across frequencies. Empirically, masking high-energy bands yields richer, more discriminative features compared to random time-based masking. driving state-of-tte the-art performance in tasks such as arrhythmia detection (66.7% % F1 score on PTB-XL (19) and muscle activity classification (94.5% % accuracy on EPN-612 20). see Section 2.4h.
Multi Multi-modal biosignal learning faces two key challenges: first, extreme heterogeneity across modali- ties like EEG, EMG, and ECG, which operate at different sampling rates and temporal scales, leading to potential misalignment and spectral aliasing (1:; second, variability in signal quality due to motion artifacts or electrode drift (22]. To overcome these, we use modality-specific backbones: a pretrained encoder for EEG and newly pretrained PhysioWave encoders for EMG and ECG. These backbones remain frozen during downstream training, with only lightweight classification heads and fusion coef- ficients being learned. The fusion layer dynamically adjusts weights to prioritize reliable modalities, ensuring robust predictions. This fincarprobing approach consistently outperforms Ainle-modaliy basclines across mudti-modal tasks (see Figure(5] including a 7.3% gain in classification accuracy on DEAP 23), showcasing the effectiveness of dynamic, reliability-awar fusion for heterogeneous physiological data.
In summary, our work leverages wavelet-basee decomposition and a FgM strategy to advance self-supervised learning in physiological signal processing. Specifically:
PhysioWave: A versatile wavelet-driven architecture for physiological signals. We propose PhysioWave, a versatile model framework applicable to diverse physiological signals, and using this architecture which accommodates physiological signals with different sampling rates and dimensions through a learnable wavelet decomposition pipeline and
2

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unified Transformer backbone, reducing the dependency modality-speciin preprocessing. A FgM mechanism is introduced selectively occlude the most informative input segments, enhancing self- sufervieee learning.
2. Enhanced representation learning. Large-scale pre-trained models for EMG and ECG have been developed, trained 823 GB of EMG data and 182 GB of ECG data, respectively, addressing the long-standing gap in foundational models for these modalities. Extensive evaluations across various tasks and datasets within each physiological modality demonstrate that PhysioWave Wave consistently achieves Mate-of-the-an performance, underscoring its broad applicability and robustness (See Section 2.4).
3. Unified multimodal framework. By integrating our own pre-trained EMG and ECG models, which were developed using the Physio Wave architecture, with existing pre-trained EEG encoders, we build a unified framework that synergistically processes and fuses multimodal signals, producing superior performance compared single-modal approaches.
Method
Skiling Window
Data Sumple CXT
Learnable Wavelet Decompasition
Lampun Filee
Adaptive Gating
Hidqen Filce
Badi Caedidan
(s))
Aduptive Gating
Cross
a*'[s]
CAPFN
Concetcnation
a*'[s]
Lowcoss FIlo!
Lampun
Hilqus Fllce
Aduptive Gatling
Cross Seale
CAFFN
Pirppin Filor
Adgetiee War Selector
H:: Multi-Scale Wavelet Feature Reconstruction
Reconstraction
sP((e)
Projection
Eacoday
Transfommrr
Head
Limur
a7/(c]
M
Exusetion
Benssegl
Frequency guidel Manking
Eparep Rea
Ramis poolerrdiiiiiig
Irepartance scomes
Figure 1: Model pretraining pipeline. The pipeline begins by initializing of standard wavelet functions (e.g., 'db6', 'sym4'), ), from which Iearnable low-pass and high-pass filters are generated. These filters are then used for wavelet decomposition obtain multi-scale frequency-bann represen- tations. The decomposed features are processed into apatio-tempord patches, with importance scores computed using FFT-based spectral energy. High-scoring patches are masked and passed through Transformer layers, followed by lightweight decoder for patch reconstruction.
Model Architecture: Figure Iillstrrates the end-to-end model pretraining pipeline. The raw multi- channel signal is first segmented into overlapping windows to form samples x E RCx, where C denotes the number of channels and T the number of time steps. Each sample undergoes a learnable wavelet decomposition across L levels, yielding multi-scale frequency- band representations: detail subbands d(0) [x] RCXT for = 1,..., I. and final approximation subband a!4) [n] CxT Concatenation of these + 1 subband outputs along the channel axis produces feature map Spoc(X) ([L+1) x (Section 2.1 Next, the feature map partitioned into uniform spatio- temporal patches: for each patch we compute its FFT-based spectral energy, blend this value with random noise to produce an importance score, and mask highest-scoring patches by replacing their embeddings with learnable <MASK> token. The remaining patches are projected into token embeddings and augmented with rotary positional embeddings (Section 2.2). Finally, the complete token sequence is passed through stack of Transformer encoder layers, and a lightweight decoder

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reconstructs the masked patches (Section 2.3 In addition, end-to-end fine-tuning is performed downstream for modallty tasks, while in multimodal settings modality-speciff predictions are aggregated to yield the final output (Section 2.4
2.1 Learnable Wavelet Decomposition
This module decomposes multichannel time series {x)(t)) of length T into L + subbands per channel by iterating Analysis, Adaptive Gating, and Feature Fusion.
Adaptive Wavelet Selector. Low-pass and high- pass filter taps h hhi RKo, where Kg the original wavelet filter length are extracted from chosen discrete wavelet using PyWavelets [24). These taps are then resampled to length K and normalized:
(u) Interp(h", K)(u,, h2 he
p flo,
(1)
|
Each channel's depthwise filters then initialized by copying these taps:
koo (u) = 1 (w), Rhigh (u)
= hhiigh())
iu) yc=1,...
(2)
As depicted in the top-left corner of Figure 1 the model maintains M candidate wavelet bases {(kle kbiggh)J.M } to accommodate diverse signal characteristics. For each input x E RCXT temporal information is aggregated via average pooling and the resulting feature vector is passed through compact MLP to produce unnormalized selection scores. Applying a softmax yields selection weights
a = Softmax (MLLL(AggPA(A E RRM
(3)
which are used compute convex combinations of the candidate filters:
Jow
Oe
k
low
k high
aw high
(4)
These aggregated filters serve as the effective low- and high-pass filters for all subsequent analysis and downsampling operations, and during training their weights continue to be updated to adapt to the actual signal distribution.
Analysis and Soft Gating. The learnable wavelet front-end performs multi-resolution analysis in which each stage halves the temporal resolution while preserving both low- and high-frequen content. Let downsampling and nearest-neighhor upsampling by a factor of be
(42 [m] = x[2n].
n O...., = , 1.
1,
(5)
(12 [m] =
At the first level (f 0) every channel is filtered and downsampled,
[n)
Σ X2(2n + u))kk Rlow [u], d!'
Σ
Xa + u) [h)s
and the process recurses for E 1,, I...,,L- 1
K-1
K-1
d (++1 [n] = Σ ( (2n + u) klow
d(+1 = Σ
0 (
(2n (2n
+ u) k. bhigh
(7)

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Global armaage Padiig
Malri Heal Alcntinn
Gafe Score for each channel
Eoregaso Filter
Highipss Filve
12
4P
[x] Carnet
[P) Neatleet
12
4"
Noutboot
Gpanpled
Adaptive Gating
xP'p]
Gaes light bovel
Figure Analysis and soft gating process. The learnable wavelet front-end performs multi-resolution analysis by filtering and downsampling the input signal at each stage, preserving both low- and high-frequency components. the first level (E 0), the signal is decomposed into low-pass and high-pass components. This process recurses for & = 1,..., L 1, applying downsampling at each level. After decomposition, the subbands are upsampled the original resolution, and an adaptive
gate G! E [0, 1] is learned for each channel using multi-head attention. The gate dynamically combines the original and upsampled signals, facilitating fine-scale detail insertion.
After each decomposition, the new subbands are upsampled back the original length
(m) (12 [n), a [m] (12 di (m),
(8)
so that fine-scale details can be re-inserted into the current resolution. Rather than performing a hard
skip connection is common in U-Net-style architectures [25] adaptive gate G(f) E [0, 1] is
estimated for every channel via multi-head attention pooling over ae (see Fig. 2. This gate weighs the contribution of the original and the upsampled signals,
(E)
af0 [n]] = G [n]
a [m) + (1 G( [nl) (()+))(n)
(9)
[00 = _ [n] de + (1- G G( (nl) deet1)
(10)
The soft-gating mechanism endows the model with three advantages: (i) it enables learnable trade- off between current-level context and finer-scale detail, reducing aliasing and ringing artefacts that often arise from naive upsampling; (ii) the gate is estimated on per-channel basis, allowing different physiological channels to emphasise frequency content most relevant to their noise characteristics; and (iii) by relying attention rather than fixed skip connections, the model dynamically modulates information flow across levels, leading to more expressive multi-scale representations.
Feature Fusion. At each decomposition level 1,..., L, the gated approximation- detail pair (a [m), [m]]]
[ni]]] is concatenated along the channel axis and reshaped into 22CxIxT fea- ture map. This map is fed to a Cross-Scale Channel-Aggregatine
Feed-Fore Forward Network (CAFFN; block and then performs multi-head attention where the current features act as queries and the flattened feature maps from all shallower levels provide keys and values. Formally, let U be the CAFFN output before cross-scale fusion; the refined representation is obtained as
see Fig. 3 (26). CAFFN first applies a lightweight
Y [n] (n] + 8 Attention (U(())) {Y (n])())))))
(11)
where the learnable scalar 3 balances the current-level information with the context aggregated from coarser resolutions. This cross-scale fusion enables fine-grained subband features to be informed by long-range patterns captured at earlier levels, yielding scale-aware, frequency-aware representations for subsequent stages.
Finally, each refined map (() is split back into its approximation and detail halves, Y [a(*), ] where ( d e ROT The multi-band representation is obtained by concatenating all detail subbands together with the final- finellevel approximation:
Spec(X) [d d(2) ..... (L)) a00) [(x-1)xXT
(12)
5

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g0/n|
lorpet
EEG
EMG
Heseet resippication
Hoseatinhe addlise
Rodital
Reshape
Imput
Axx
FRG Enceder
EEG Encuder
EFG Encoder
Coay 3033
GRI I
Clamification Head
Clasification Head
Chselicction Head
Channel Aggregation
Conv
[+[
Pyer
X 8
0Es
Multi-Head Self Attention
and final chusification
Output
y [p]
Cross-Scale CAFFN: This module refines multi-resolution features using convolution, channel aggregation, and self-attention.
Figure 3:
Figure 4: Multi-mode framework: Classification of driving behaviors the MPDB dataset.
2.2 Frequency-guide Masking and Tokenisation
Frequency-guided masking. To address the challenge of selectively occluding informative seg- ments, introduce the trequency-guidee masking process, detailed in Algorithm [ The multi- band feature map Spec(X] from Eq. (1) is first sliced along the time axis into N = [T/w non-overlapping segments of length w, yielding patch array of shape Nxw for each signal. In the next step, the FFT is applied to each patch to capture its frequency components. The energy of each patch in the frequency domain then computed, which serves an indicator of its importance. However, relying solely on frequency energy could lead to overfitting, especially in scenarios where necessarily the most informative. To mitigate this, random noise introduced into the process. This randomness is controlled by the parameter o, which governs the trade-off off between the energy and the random noise [16]. The mask ratio, p. determines the proportion of patches to be masked. Together, the combination of frequency seruuuenyy-uieedd guided masking and random noise ensures that the model learns focus on the most informative parts of the signal, while also being robust to variations and missing information.
certain high-energy patches
Tokenisation.
Each masked unmasked patch is projected to D-dimensional token through shared linear layer, forming the matrix E E IR N Rotary positional embeddings {s, ) C R are then added.
En F,, Sn.
1,...,N. N.
(13)
The resulting sequence É NxD, together with the mask M, is forwarded to the Transformer encoder, which must reconstruct the masked, information-rich patches from context, thereby encour- aging robust and frequency representation learning.
2.3 Reconstruction
Transformer encoder and lightweight decoder. The encoder comprises L standard Transformer blocks 27 where the only change is that the attention module uses RoPE attention. In RoPE attention query and key sub-vectors of every head are rotated by deterministic sinusoidal factors that depend on their relative positions [28). This rotation preserves dot- dot-product magnitudes while

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Algorithm Frequency-guidee masking
Require: Signal e RCXT patch width w, mask ratio p. importance weight
1: N + [T/w]
> number of patches
2: Slice the time axis into N non-overlapping patches PI....,PP R
to N do 4: F F+-FFF FFF pn.)
3:
for
score each patch
frequency spectrum
5: En - - Fx Fkl
spectral energy
6: end for
min
7: Normalise e + max min(e)
8: Draw noise 2n ~ (1, 1) for n = ,,.., N
9: 8n a. n + (1 0:) Zn
blended score
10: + argsort(s)
D ascending: low score first patches to keep
11: L [11 N
12: keep + i[I:L], mask < i[L:N]
Initialise
1N; m m[mask] 0
Di binary mask
14: Replace each pv, with <MASK> m. = 0
15: Re-assemble the patched signal P E RN
16: return masked patches P. mask m, sort indices i
injecting position information, enabling each head to capture long-range temporal structure [29 After the L blocks the encoder outputs latent sequence in which the masked tokens already carry context-inferred estimates
[3 31.
A deliberately shallow decoder then projects the latent width to Dice, refines it with Laee vanilla Transformer blocks, and uses final linear head reconstruct every element of each patch, yielding PE N Concentrating depth on the encoder while keeping the decoder light forces the model to store most semantic and spectral knowledge in the shared latent space
[32
Patch-level reconstruction loss. Let (p,, Ln and 1.V be the ground- truth and reconstructed frequency- -band patches defined in Section 2.2 and let M {n mn = 1} be the masked- patch index set. The model trained to minimise the mean Smooth-L.1 discrepancy over only the masked patches
33 34
L
Σ
SmoothL1(p...P P..)
(14)
M
This objective comeeel the network to recover the multiscale, frequency- band information con- cealed by the mask.
2.4 Methods for downstream tasks
Single-modal setting. For tasks that involve single modality (EMG or ECG), the pretrained encoder is fine-tuned end-to-end. All patch tokens produced by the final encoder block are aggregated through mean pooling to obtain global representation, which is then fed lightweight two-layer MLP to yield the final classification prediction.
Multi-modal setting. As illustrated in Figure 4 every pretrained encoder is kept frozen and only its dedicated classification head-along with a set of fusion coefficients-ii trained. This linear- probing strategy preserves the representations learned during pretraining while mitigating the risk of wver-fitting that often arises when a large-parameter model is adapted to a small downstream dataset (5). Let M denote the set of modalities present in a particular experiment. Each modality m E M produces logits Zm and the learnable fusion weights a = {am JMEM are constrained by softmax so that Em Emem Om 1. The final prediction vector is obtained by
Ziused
Σ
Om Zm
(15)
7

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and Ztused is subsequently used to infer the task categories.
3 Experiments and Results
3.1 Datasets and Training Settings
Leveraging the Physio Wave architecture, two large-scale, modality-specifie foundation models, Physio Wave-ecg and Wayeee,, are pretrained on the most extensive open-access corpora currently available for their respective signal types (see Tables and PhysioWave is trained on approximately 182 GB of twelve-lead ECG recordings, while Wave-em utilizes about 823 GB of EMG data. For each modality, we provide three parameter configurations: Small (5M), Base (15M), and Large (37M). Both PhysioWave models share the same Transformer backhone; however, their learnable wavelet front-ends modality-aware (see Tables 12 and T3 in Appendix C for full architectural and training details).
Signal Preprocessing. Each recording is denoised with modality-specific band-pass filter fol- lowed by 50 Hz notch. Traces with fewer than 12 ECG leads or 16 EMG electrodes are zero-padded and then resampled 500 Hz (ECG) kHz (EMG). See Appendix D and for full details.
Downstream Tasks and Evaluation Protocol The pretrained encoders are evaluated on the datasets listed in Table 17 All downstream experiments follow the single- and multi-modal pro- cedures detailed in Section 2.4 Each benchmark is split by subject into 6:2:2 train/validation/test
partitions prevent subject leakage.
3.2 Downstream Experiment Results
ECG Multi-label Classification Results. Table compares the proposed Physio Wave-ecg with recent large-scale pretrained ECG models. The Small model (5 M) already delivers competitive performance: on PTB-XL it raises Fl from 55.9 % (ECG-Chat [35. 13 B) to 65.8 % (+9.9 %), while attaining comparable AUROC (92.7 % vs. 94.1%). Increasing capacity Base and Large yields consistent gains; the 37 M variant sets new best scores on two of the three benchmarks, achieving 66.7 % 99.6 (FI/AUROC) on PTB-XL and 54.8 % 98.3 % on Shapman-Shaakee 2018 it surpasses the previous AUROC ceiling by +0.4 % (96.1 % vs. 95.7 %) and narrows the F1 gap ECG-Chat from 80.1 1 to 73.1 %.
On CPSC
Table 1: ECG rhythm classification results on three benchmark datasets.
PTB-XL
CPSC 2018
Chapman-Shaoxing
Method (year)
Params
F1
AUROC
F1
AUROC
F1
AUROC
ECG-Chat (2024)
13B
55.9
94.1
80.1
95.7
MERL (2024)
11M
48.1
91.9
72.8
92.6
87.9
MacFE (2023)
9M
64.7
88.6
71.6
94.5
OpenECG-SimCLR
11M
46.9
91.5
73.1
92.4
52.3
95.1
OPenECG-BYOL
11M
47.7
91.1
72.8
92.6
51.5
94.8
OpeNECG-MAE
11M
48.1
90.9
74.5
93.2
50.8
94.2
Ours-Small
5M
65.8
92.7
71.6
95.5
52.1
96.4
Ours-Base
15M
64.5
93.4
72.5
95.9
53.8
97.2
Ours-Large
37M
66.7
94.6
73.1
96.1
54.8
98.3
Note:
Pink
indicates the best results,
blue indicates the second-bes results.
Table 2 benchmarks the proposed Physio against the only publicly released large-scale
generic time-series
(385 M) and 077S (45 M) becaue no foundation model has
yet been pretrained specifically for EMG signals 36 Even with just 5 M parameters, the Small variant already surpasses both baselines the challenging EPN-612 dataset (93.1% % 93.4 % versus

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Table Surface-EMG gesture recognition performance across three datasets.
NinaPro DB5
EPN-612
UCI EMG
Method (year)
Params
Acc.
F1
Acc.
F1
Acc.
F1
Moment (2024)
385M
86.41
74.42
90.87
90.16
90.45
91.75
OTiS (2024)
45M
85.31
72.61
87.55
88.03
90.62
89.28
Ours-Small
5M
84.78
72.54
93.12
93.40
90.35
89.51
Ours-Base
15M
86.02
73.78
93.68
93.91
91.92
92.77
Ours-Large
37M
87.53
75.42
94.50
94.56
93.19
93.59
Note:
Pink indicates the best results, blue indicates the second-bee results.
90.9 % / 90.2 % for Moment). Scaling up the encoder brings steady gains: the 37 M Large model achieves new state-of-the-ar results on all three Saatimmars--naPPPP DB5 (+1.1 accuracy over Moment), EPN-612 (+3.6 %), and UCI EMG (+2.6 % over O still using less than 1/10 of Moment's parameters.
Multi-modal Classification Results. The proposed multi-modal framework built from the Small PhysioWave-ecg and Physio Wave-emg encoders together with an EEG branch (EEGPT for DEAP, LaBraM for MPDee-aahieves the best accuracy 23 37
On DEAP, fusing the EEGPT backbone with the Small PhysioWave ECG/EMG encoders lifts valence accuracy from 79. 19.1% to 85.2% (+6.1%) and arousal accuracy from 81.3% 88.6% (+7.3%), as shown in Figure 5 The multimodal system also surpasses TPRO-Net by 0,4 % and outperforms Bi- ~atMeattention and Bayesian pipelines by 7-22 % 38
underscoring the benefit of a multimodal
framework over single-modality models.
On MPDB, the same multimodal architecture obtained by replacing the EEG branch with LaBraM (5.8 M improves accuracy from 70.4% (using LaBraM alone) to 74.9% (+4.5%), as shown in Figure This outperforms generic sequence models such MMPNet and EEGNet by 9%
to 17%
37 39
DEAP Dataset
(b) MPDB Dataset
88.6
74.9
90
Arousal
85.2
Accuracy (%)
Accuracy
75
70
66
Baysslan
BI-LSTM +Atth
EEGPT
TPRG-Net
Oum
EBCNN
CNN
LSTM
MHihWet
LaftraM
Ours
Figure 5: Multimodal classification performance.

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3.3 Ablation Experiment Results
Table Ablation on Waye-em for EPN-612 dataset.
Configuration
Train Loss
Accuracy (%)
F1 (%)
w/o Trequency-guided Masking
0.24
92.48
92.85
w/o pre-training
0.27
91.67
91.57
with all
0.22
93.12
93.67
As shown in T removing either design method degrades performance. Comparing random masking with the same mask ratio of 0.7, discarding the frequency-guide masking strategy reduces FI by 0.8%, while training the network from scratch (without pretraining) results a 2. 1% decrease F1 and increases the training loss. The complete PhysioWave model thus benefits from both Frequency- guided Masking and large-scale self-supervised pretraining, achieving the highest accuracy and F1 score EPN-612 dataset.
4 Conclusion
In conclusion, this work introduces PhysioWave, a novel wavelet-based architecture designed to enhance physiological signal processing by leveraging adaptive multi-scale decomposition and frequency-guided masking to advance self-supervised learning. The proposed model demonstrates state-of-the-at performance across both single-modality tasks, such as EMG and ECG classification, and in multi-modal settings that integrate EEG, EMG, and ECG signals. Our results underscore PhysioWave's capacity effectively capture the unique, time-varying characteristics of physiological signals, addressing key challenges such as non-stationarity. low signal-to soll-to-noiee no-noise ratios, and high inter-subject variability. By combining wavelet decomposition with rrequency-guider masking, Physio Wave improves feature extraction, making it particularly well-suited for real-world applications where these challenges are prominent.
Furthermore, the proposed framework sets new benchmarks for EMG and ECG analysis and estab- lishes strong foundation for future work in multi-modal biosignal processing. With its ability to adapt to heterogencous and noisy physiological data, PhysioWave holds significant promise for range of applications, including health monitoring, clinical diagnostics, and personalized medicine.
Ethics Statement
This study exclusively utilizes publicly available, fully de-identified physiological signal datasets (EEG, EMG, and ECG) with no collection new data or access to personally identifiable information. All datasets were obtained in compliance with their respective terms of use, and the original data collections were conducted under appropriate institutional review board (IRB) approvals as docu- mented in their publications: NinaPro (Ethics Commission of Canton Valais), MIMIC-IV-ECG (Beth Israel Deaconess Medical Center and MIT IRBs), PTB-XL (PTB Institutional Ethics Committee), DEAP (Queen Mary University of London Ethics Committee), and MPDB (Tsinghua University Medical Ethics Committce). Our secondary analysis of these anonymized public datasets adheres to the NeurIPS Ethics Guidelines, with re-identification attempts made.
6 Acknowledgment
This research was partly supported by the EU Horizon Europe project IntelliMan (g.a. 101070136), the PNRR MUR project ECSOOO003ICOSIITER, and the ETH Zürich's Future Computing Laboratory funded by a donation from Huawei Technologies. Furthermore, the research was also partially supported by the EU Horizon Europe project HAL4SDV (g.a. 101 139789).
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A
ADDITIONAL EXPERIMENTS
A.1
Ablation Study
To validate the effectiveness of each proposed component in PhysioWave, conduct comprehensive ablation experiments by systematically removing or replacing specific sub-modules. All experiments performed using the pretrained small PhysioWavs Wave model with fine-tuning on the target datasets.
A.2 Component Analysis
We analyze three key components: Soft Gate (SG), Channel Aggregation Seed-Forward Network (CAFFN), and Adaptive Wavelet (AW). When AW is disabled, we use fixed db6 wavelet decompo- sition the baseline.
Table 4: Main ablation study results EPN612 dataset showing individual and combined component contributions.
Components
Performance
SG
CAFFN
AW
Accuracy (%)
F1-Score (%)
Improvement
X
X
X
89.88
89.76
X
X
91.71
91.66
11.83%
X
V
X
91.53
91.46
t1.65%
X
X
V
92.23
92.22
12.35%
X
V
V
93.12
93.10
13.24%
V
X
92.64
92.89
12.76%
x
92.52
92,48
12.64%
93.85
93.51
13.97% %
Soft Gate, CAFFN Channel Aggregation FFN, AW = Adaptive Wavelet. Pink/blue highlights indicate the best and sccond-best results.
The results demonstrate that Adaptive Wavelet contributes the most individually (+2.35%) high- lighting the importance of adaptive wavelet selection fixed approaches. The complete model achieves optimal performance (93.85% accuracy), showing synergistic effects when all components combined.
A.3
Gate Mechanism Comparison
We compare different gating strategies to validate our soft gate design choice.
Table 5: Gate mechanism comparison on EPN612 dataset.
Gate Type
Threshold
Accuracy (%)
F1-Score (%)
Accuracy Drop
Hard Gate
0.3
92.07
92.05
41.78%
Hard Gate
0.5
92.25
92.23
11.60%
Hard Gate
0.7
92.17
92.19
41.68%
Gumbel Gate
0.5
91.47
91.51
12.38%
Soft Gate
93.85
93.51
Note:
Accuracy drops are compared against gate baseline. Pink indicates the results, blue indicates the second-best results.
Our soft gate mechanism significantly outperforms alternatives, with hard gates showing thresh- old sensitivity and Gumbel gates exhibiting the largest performance degradation due stochastic sampling being less suitable for physiological signals.
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A.4
Feed-Forward Network Variants
We evaluate different FFN architectures to demonstrate the effectiveness of our CrossScale CAFFN design.
Table 6: Feed-forward network variant comparison EPN612 dataset.
FEN Variant
Accuracy (%)
F1-Score (%)
Accuracy Drop
Standard FFN
91.95
91.93
11.90%
CAFFN
92.72
92.69
11.13%
CrossScale No Attention
93.29
93.31
40.56%
CrossScale CAFFN
93.85
93.51
Note: Accuracy drops are compared against CrossScale CAFFN, Pink indicates the best results, blue indicates second-best results.
The progressive improvements from Standard FFN to CrossScale CAFFN demonstrate the value of each enhancement: channel aggregation (+0.77%), cross-scale fusion (+1.34%), and attention mechanisms (+1.9.%)
A.5 Wavelet Selection Analysis
We investigate the impact of different wavelet configurations on both EMG and ECG modalities to validate our adaptive wavelet selection approach.
Table 7: Wavelet selection ablation study EPN612 EMG dataset.
Wavelet Configuration
Accuracy (%)
F1-Score (%)
Improvement
Random Xavier (Baseline)
92.25
92.22
Single Wavelet db6
92.13
92.11
J0.12%
coif4
92.13
92.14
J0.12%
sym6
92.93
92.93
10.68%
Waveler Combinations
db6+sym6+coif4
93.51
93.51
11.26%
db4+db6+sym4
93.90
93.90
11.65%
dbS+coif4+bior4.44
92.90
92.89
10.65%
Note:
Pink indicates the hest results, blue indicates the second-hest results.
The results reveal that single wavelets provide minimal improvements (<1%), while multi-wavelet combinations achieve substantial gains (1.65% for EMG, 2.00% for ECG). Notably, optimal combi- nations differ between modalities: EMG benefits from db4+db6+sym4 for capturing transient muscle activations, while ECG prefers db6+coif4+sym8 for complex cardiac morphologies. This validates our modality-aware adaptive wavelet selection strategy.
A.6 Modality-Speciffe Adaptation for EEG and PPG Signals
To demonstrate the generalizability our PhysioWave framework beyond ECG and EMG signals, we conducted experiments on EEG and PPG modalities. These experiments validate our model's ability to adapt to diverse biosignal characteristies without requiring -odality-speciffic architectural changes.
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Table Wavelet selection ablation study on Georgia ECG dataset.
Wavelet Configuration
Accuracy (%)
F1-Score (%)
Improvement
Random Xavier (Baseline)
62.38
48.75
Single Wavelet db4
62.70
48.94
10.32%
demy
63.59
49.36
1122 %
sym4
62.45
48.99
10.07%
Wavile-Wvvele Combinations
dh6+coif4+sym8
64.38
50.65
12.00% %
dbS+sym.54cooff+ddmy
64.20
50.46
+1.82%
sym4-synS--b--oooi34bbrrr.
63.70
49.93
11.32%
Note: Pink indicates the best results, blue indicates the second-best results.
A.7
EEG Sleep Stage Classification
A.7.1 Dataset and Setup
We evaluated PhysioWave on the Sleep-EDF dataset for 5-class sleep stage classification|40) EEG signals present unique challenges including low signal-to-noise ratio, high channel count, and significant heterogeneity across subjects. Our PhysioWave EENe--EGG---- configuration uses diverse wavelet bases (db4, db6, sym4, coif2, bior2.2) suited for EEG characteristics, with transformer backbone (embed dim==12, depth=8, num heads=8).
For the enhanced version, incorporated three key improvements:
Multi-Scale Temporal Enhancement. Added multi-scale convolutions to capture EEG's diverse frequency bands (delta, theta, alpha, beta, gamma) and improve SNR
Channel Interaction Modeling: Enhanced spatial relationship modeling between EEG electrodes through cross-channel attention
Window Attention:
Implemented efficient processing of long EEG sequences with linear
complexity
A.7.2 Results and Analysis
Table 9; EEG sleep stage classification results on Sleep- Sleep-EDF dataset. All baseline methods use pretrained models, while Physio is trained from scratch.
Method
Balanced Accuracy
Cohen's Kappa
Weighted F1
BENDR (pretrained)
0.6655
0.6659
0.7507
BIOT (pretrained)
0.6622
0.6461
0.7415
LaBraM (pretrained)
0.6771
0.6710
0.7592
EEGPT (pretrained)
0.6917
0.6857
0.7654
PhysioWave (original)
0.6720
0.6676
0.7472
PhysioWave (enhanced)
0.7312
0.7206
0.7839
Note:
Pink indicates best results, blue indicates second-best results.
As shown in Table 9 even the original Physio Wave architecture achieves competitive performance against EEG-specific foundation models. The enhanced version achieves substantial improvements: +5.7% in Balanced Accuracy, +5.1% in Cohen's 's Kappa, and +2.4% in Weighted Fl compared to the best baseline (. Notably, these gains are achieved through direct training from scratch, without the computational overhead of large pretraining required by bascline methods.
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The performance improvements demonstrate that our wavelet-based based approach effectively captures the multi-scale temporal dynamics inherent in EEG signals. The enhanced attention mechanisms successfully model inter-channel dependencies crucial for sleep stage classification, where different brain regions exhibit coordinated activity patterns.
A.8
PPG-Based Activity Recognition and Heart Rate Estimation
A.8.1 Dataset and Architecture
We evaluated PhysioWave on the PPG-DaLiA dataset, which contains PPG signals collected during various physical activities[AT]. Given the deployment constraints of wearable devices, designed PPy as a compact variant optimized for edge computing:
Reduced Model Depth: transformer layers (vs. 8-12 in standard configurations)
Smaller Embedding Dimensions: 192 (vs. 256-512)
PPG-Optimized Wavelets: db4, coif2, bior2.2 selected for cardiovascular periodicity
A.8.2
Activity Recognition Results
Table 10: Activity recognition performance on PPG-DaLiA dataset.
Method
Params
Accuracy
Precision
Recall
F1 Score
AUROC
Pulse- PPG (pretrained)
28.5M
0.4234
0.4398
0.3663
0.3648
0.8246
Physio Wave-PPG
2.53M
0.6209
0.6015
0.6077
0.5952
0.9420
Note: Pink indicates best results.
A.8.3 Heart Rate Regression Results
Table 11: Heart rate regression performance comparison.
Method
Params
MAE
MSE
MAPE
Pulse-PPG (pretrained)
28.5M
3.705
59.81
4.84%
Deep PPG
8.5M
7.65
NAS-PPG
0.8M
6.02
PhysioWave-PPG
1.57M
5.22
65.62
5.83%
Note:
Pink indicates best results, blue indicates second-hest results.
A.8.4
Performance Analysis
For activity recognition (Table TÕP. PhysioWave-PPG achieves remarkable improvements despite using 11.3x fewer parameters than Pulse-PPG:
+63.1 % F1-score improvement: 0.5952 vs 0.3648
+14.2% % AUROC improvement: 0.9420 vs 0.8246
+46.7% % accuracy improvement: 0.6209 vs 0.4234
For heart rate regression (Table I.p. our compact 1.57M parameter model achieves competitive
performance, outperforming Deep PPG and NAS-PPG while approaching performance of 28.5M parameter Pulse-PPG model [1 The small performance gap (MAE: 5.22 3.705) acceptable given the 18.2x parameter reduction, making PhysioWave-PPG ideal for wearable deployment.
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A.9 Key Insights
These additional experiments demonstrate three critical capabilities our PhysioWave framework:
The framework successfully adapts to signals with vastly dif- ferent characteristics, from complex multichannel EEG with low SNR to periodic, single- channel PPG with clear cardiovascular patterns.
Modality Generalization:
2. Efficiency Without Pretraining: PhysioWar Wave achieves competitive or superior perfor- mance compared to pre-trained foundation models while training from scratch, eliminating the computational burden of large-scale pre-training.
3. Scalable Architecture: The framework scales effectively from compact edge deployments (1.57M parameters for PPG) more complex clinical applications (enhanced EEG model), maintaining strong performance across the spectrum.
The success in the EEG and PPG modalities, combined with our primary ECG and EMG results, validates Physio Wave truly universal biosignal processing framework. The savelet-transfommet synergy provides the necessary flexibility to capture modality-speciiff patterns while maintaining a consistent architectural foundation.
B RELATED WORK
B.1 Self-supervised pre-training for Time Series
Self-supervised learning for time series data has seen considerable advances with the development of models such MOMENT and OTIS, which are designed handle the unique characteristics of time-series signals, such as non-stationarity. noise, and high variability across domains. Both models leverage large-scale pretraining to learn generalized representations for wide range of tasks.
The MOMENT model, for instance, introduces a self-supervised learning framework for time series data using masked representation learning. Capture both local and global temporal dynamics through its ability to reconstruct masked input sequences. MOMENT has been shown to perform well in tasks like classification, anomaly detection, and forecasting, Furthermore, it applics contrastive learning to model dependencies and trends in the data, addressing challenges such as long-range dependencies in time series sequences [2]
without task-specifie supervision.
OTiS, on the other hand, extends the idea of self-supervised learning to multi-domain time-series data. Its pre-training paradigm incorporates domain-speciffe tokenization and dual masking strategy to handle the heterogeneity across time series from different domains, including EEG, audio, and financial data. Using a domain-specific tokeniser with learnable signatures, OTiS can capture domain- specific data characteristics while learning generalized features. This allows to excel various downstream tasks, such as classification, regression, and forecasting, in multiple domains (36).
Both models highlight the importance of large-scale pretraining on diverse datasets to unlock basic modeling capabilities. MOMENT and OTiS aim to generalize across domains, mitigating issues caused by domain-specifi differences such as sampling frequencies and intervariate relationships. These models represent significant strides in time-series analysis, particularly in scenarios where labeled data are scarce, but large, diverse, and unlabeled datasets are available for pre-training.
This self-supervised approach provides robust solution for time-series data, leveraging vast datasets for pretraining and fine-tuning these models on specialized tasks, thus achieving state-of-thh-art performance in range of applications from health monitoring to forecasting.
B.2 Self-supervised pre-training for EEG signal
Self-supervised learning has gained significant traction, particularly in natural language processing (NLP) and computer vision (CV), and has shown promising results in the domain of electroen- cephalography (EEG) 39 EEG, self-supervised learning models are leveraging the ability to pretrain models large, unlabeled datasets, which can then be fine-tuned for specific down- stream tasks. Two notable models in this domain are EEGPT and LaBraM, both of which advance self-supervised learning techniques for the analysis of EEG signals.
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EEGPT (EEG Pretrained Transformer) employs masked autoencoder- based self supervise learn- ing strategy specifically designed for EEG data. This model trained predict masked segments of the EEG signal, enabling it to learn robust representations of the signal's spatiotemporal fea- tures. The dual self-supervised learning approach in EEGPT combines masked reconstruction and spatiotemporal alignment, effectively addressing the challenges posed by low signal-to-nois ratio (SNR) and intersubject variability in EEG, This method allows EEGPT to perform well in a variety of EEG-based tasks, particularly in the context of brain-computer interface (BCI) applications where accurate feature extraction is critical [
LaBraM (Large Brain Model) is another significant model in self--upprrvsed EEG pre-training. LaBraM focuses on learning universal EEG representations through unsupervised pre-training on large-scale EEG datasets from various domains. To address the challenge of pross-dttaset learning, LaBraM segments EEG signals into channel patches and uses a vector-quie rector-quantzed neural spectrum prediction model to generate compact neural tokenizer. This allows the model to learn generalized representations that can be fine-tuned on specific tasks, such as abnormality detection, emotion recognition, and gait prediction. The model's ability to handle diverse EEG data with varying channel configurations and recording settings has been validated in multiple EEG-based tasks, demonstrating its robustness and scalability 4
Both models underscore the importance of large- segle-ccae pretraining the EEG domain. By learning from vast amounts of unlabeled data, these self-supervised models can capture the rich, multi-scale features of EEG signals, which are crucial for wide range of applications in health monitoring and BCI systems. The ability of EEGPT and LaBraM to generalize across datasets and tasks highlights the potential of seff-supervised learning to transform the analysis of EEG signals, making deep learning techniques more applicable and effective in the field of biosignal processing.
B.3 Self-supervised Pre-training for ECG Signal
Self-supervised learning (SSL) has become a key method in the development of foundational ECG models, addressing challenges such as the scarcity of labeled data and the need for robust gener- alization in diverse datasets. Recent advancements have introduced several effective SSL-bbased pretraining strategies for ECG data.
One such approach is OpenECG, which created a large ECG benchmark with over 1.2 million 12-lead ECG recordings from 9 centers. The study evaluated three prominent SSL methods: SimCLR, BOL,, and MAE using ResNet-50 and Vision Transformer (ViT) architectures. The results showed that pretraining on diverse datasets significantly improved model generalization, with B BYL and MAE outperforming SimCLR. These methods demonstrated superior effectiveness in learning feature consistency and generative representations compared to contrastive methods, which require large datasets to perform optimally 44
Another notable model is ECG-Chat, a large ECG-language model designed for cross-modal cardiac diagnosis. It combines ECG waveform data with text reports using contrastive learning to align ECG features with medical text. The model was trained on a data set that integrates both diagnosis and conversation tasks, achieving state-of-the-art results in the generation of medical reports from ECG. ECG-Chat also incorporates novel method to mitigate hallucinations during report generation by in- tegrating external cardiology knowledge via GraphRAG and DSPy components. This ensures that the generated ECG reports are grounded in clinical knowledge, enhancing accuracy and reliability (35).
These models highlight the potential of self-supervised learning to improve ECG analysis and improve diagnostic tools. Using large-scale ECG data and pretraining strategies, both OpenECG and ECG- Chat advance the capabilities of ECG models, making them more robust and adaptable across a range of clinical and research applications. They represent significant step toward improving the accessibility and accuracy of Al-driven cardiovascular diagnostics.
C HYPERPARAMETER SETTINGS
We employ Adam W optimizer with a weight decay of 0.01 and moment coefficients 31 = 0.9 and 82 = 0.98. The learning rate is linearly warmed up from 5 x 10-7 to 5 x 10-3 over the first ten epochs and then follows a cosine decay to floor of x 10-6, Pretraining lasts for 50 epochs with a global batch size of 64 on 16 NVIDIA A100 GPUs, whereas all downstream experiments are carried
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out on 4 A100 GPUs. During downstream training, the same A optimizer and cosine scheduler are retained, but the learning rate is reduced to 1/10 of its pretraining value.
Table Hyperparameters for masked ECG pre-training with PhysioWave-ecg
Hyperparameter
-Saalll PhysioWave-ess Base
Wavelet Front-End
Input channels
12
12
12
Max decomposition level
4
4
4
Wavelet kernel size Wavelet bases
24
24
24
[sym4, db4, db6, coif2)
Transformer Encoder
Timesteps
2048
Patch size (H x W)
[1. 6.4
Embed dimension (= hidden size)
256
384
512
Encoder layers
8
Attention heads
8
12
MLP ratio
4.0
MI.P size
024
1,366
2048
Drop-path
0.10
Reconstruction Head
Hidden dimensions
[256]
Output dimension (patch dim)
768 (12 channels timesteps)
Activation function
GELU
Dropout
0.1
Layer normalization
True
Pre-training Setup
Batch size
64 5 10 5
Peak minimal learning rate
10
Optimizer (/1, 32)
AdamW (0.9, 0.98)
LR scheduler
Cosine
Weight decay
0.01
Total warm-up epochs
50 10 64
Accumulated grad batches
Gradient clipping
Mask ratio / importance ratio
0.70 0.60
Max sequence length
2048
D PRETRAINING DATASETS DESCRIPTION
D.1 Pretraining datasets for PhysioWave-ecg
In the pretraining of ChysioWWve-FECC we streamline the data processing pipeline by leveraging the inherent filtering capabilities of our wavelet-based architecture. Unlike conventional approaches that require extensive preprocessing with bandpass filters, notch filters, and baseline wander removal, our adaptive wavelet decomposition naturally handles these signal conditioning tasks within the model itself. The process involves two main steps. First, we use a sliding window technique where ECG signals with 12 leads are divided into overlapping windows of 2048 samples. If the signal length is insufficient for a full window, we apply zero-padding to maintain consistency across the dataset. Second, for ECG recordings with sampling rates different from our target 500 Hz, we resample the signals to ensure uniformity across the dataset. Notably, we intentionally omit traditional filtering operations (such 0.05-100 Hz bandpass filtering 60 Hz notch filtering) from our preprocessing pipeline. This is because our multi-level wavelet decomposition with learnable basis functions serves as an adaptive, dita-driven filtering mechanism. The wavelet transform inherently provides frequency- selective decomposition, effectively separating signal components across different frequency bands. Our learnable wavelet kernels adapt during training to optimally filter noise, remove baseline wander, and preserve clinically relevant signal features-aa without manual filter design or fixed frequency cutoffs. This approach not only simplifies the preprocessing pipeline but also allows the model to leam task-specific filtering strategies that may be more effective than generic, crad-crafted filters.
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Table 13: Hyperparameters for masked EMG pre- are-training with Physio Waveremev.
Hyperparameter
PhysioWave-emg-Smal
Physio Wave-ewg--eass PhyxioWavv-emg-laarg
Waveler Front-End
Input channels
Max decomposition level
Wavelet kernel size Wavelet bases
16
(db4, bior4.4, symS, coifS
Transformer Encoder
Timesteps
1024
Patch size (H W)
(1. 64)
Embed dimension (= hidden size)
256
384
512
Encoder layers
8
12
Attention heads
12
16
ML.P ratio
4.0
MLP size
048
Drop-path
0.10
Reconstruction Head
Hidden dimensions
[256]
Output dimension (patch dim)
512 (8 channels x 64 timesteps)
Activation function
GELU
Dropout rate Layer normalization
0.1
True
Pre-training Setup
Batch size
64 10 / 10-6
Peak minimal learning rate
Optimizer (31. (22)
AdamW (0.9, 0.98)
LR scheduler
Cosine
Weight decay
0.01
Total / warm-up epochs
50 / 10
Accumulated grad batches
64
Gradient clipping
3
Mask ratio/ importance ratio
0.70 / 0.60
Max sequence length
2048
Table 14: Hyperparameters for downstream fine-tuning with Physio Wave. Hyperparameter
Value
Batch size
32
Peak / minimal learning rate
5 10-4 / 10
Learning rate scheduler Optimizer (31, (82) Weight decay
Cosine
AdamW (0.9, 0.98)
0.01
Total epochs
Early stopping (max 50)
Warm-up epochs
Drop-path
0.10
Layer-wise learning rate decay
0.90
Label smoothing (multi-class classification)
0.10
Finally, the processed ECG data is saved in the HDF5 format, organized into a dataset with dimensions corresponding to the batch size, number of leads (12), and window size (1024 samples). This preprocessed data is then used train the PhysioWave-ecg model.
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Table 15: 12-lead ECG corpora used for pretraining.
Dataset
Subjects
Records
Dur. (s)
fs (Hz)
Size
MIMIC-IV-ECG 45
~600000
8000000
10
500
90.4 GB
MedalCare-XL [46]
13
16900
10
500
26.2 GB
CODB-15%
47
233 770
779
10
400
63.3 GB
Norwegian Athlete
48
28
28
10
500
52.8 MB
Georgia Cohort
49
10344
10344
10
500
1.2 GB
The MIMIC-IV-ECG module includes around 800,000 12-lead ECG recordings from nearly 160,000 patients, spanning from 2008 to 2019, Each ECG is 10 seconds long, sampled at 500 Hz, and matched with clinical data and cardiologist reports. The dataset provides not only the ECG waveforms but also sechiine-geneatted measurements such as RR intervals and QRS onset times, with all data de-identified to comply with HIPAA standards. The data stored in the WFDB format for easy processing 45
MIMIC-IV-ECG
MedalCare-XL MedalCare-XX XL is a synthetic 12-lead ECG dataset containing 16,900 10-second ECGs across one healthy control and seven pathological classes. The dataset includes ECG signals in three variations: raw, with noise, and filtered using highpass and lowpass Butterworth filters. Additionally, provides parameter files detailing the electrophysiological model used for simulation. This dataset is valuable for training ECG analysis models, especially for personalized disease simulations 46
CODE-15% The CODE-15 dataset is stratified subset of the CODE dataset, containing 345,779 12-lead ECG records from 233,770 patients, collected between 2010 and 2016. widely used for ECG automatic diagnosis research and cardiovascular risk prediction. The scale and annotation quality make it a reliable resource for developing ECG Al algorithms 47
Norwegian Athlete ECG Database The Norwegian Athlete ECG Database contains 12-lead ECG recordings from 28 elite athletes in Norway. Each ECG is 10 seconds long and recorded with GE MAC VUE 360 electrocardiograph. The dataset includes both machine geenertte interpretations and cardiologist reviews. The cohort consists of rowers, kayakers, and cyclists, with participants aged 20-43 years. The data helps address challenges in ECG interpretation for athletes, who often exhibit heart adaptations that can mimic pathological changes [88]
Georgia Cohort The Georgia Cohort dataset contains 10,344 12-lead ECGs from male (5,551) and female (4,793) patients, each 10 seconds long with sampling rate of 500 Hz. The dataset is used for cardiovascular disease prediction and ECG classification tasks. The data offers diverse set of ECG signals, providing an opportunity to study the effects of various cardiovascular conditions across different demographic groups 44
D.2 Pretraining datasets for Physio Wave-eme
Table 16: Surface-EMS corpora used for pretraining.
Dataset
Subjects
Records
Dur. (s)
fs (Hz)
Channels
Size
NinaPro DB6
[5
~8.4
4
2.000
14
20.3 GB
NinaPro DB7
55
22
5544
5
2000
12
30.9 GB
NinaPro DB8
(52
12
~2.44
7.5
1111
16
23.6 GB
EMG2Pose
531
193
25.253
60
2000
16
431 GB
EMG2Qwerty
[54]
108
1 135
080
2.000
16
317 GB
For the pretraining of the PhysioWave-EMG model, we adopt streamlined preprocessing approach that leverages our wavelet-based architecture's inherent signal processing capabilities. Unlike traditional EMG processing pipelines that require extensive filtering operations, adaptive wavelet
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decomposition naturally handles noise suppression and feature extraction within the model itself. Our preprocessing focuses on data standardization rather than filtering. We apply z-score normalization to ensure each EMG channel has mean of zero and a standard deviation of one, standardizing the input scale.
Notably, we intentionally omit conventional bandpass filtering (20-450 Hz) and notch filtering (50/60 Hz power line interference) from our preprocessing. This is because our learnable wavelet transform provides adaptive, data-driven filtering that automatically separates relevant EMG signal components from noise and artifacts. The multi-level wavelet decomposition with trainable kernels learns to isolate muscle activation patterns while suppressing interference, effectively replacing manual filter design with learned, task-specific signal conditioning. To prepare the data for training, we apply a sliding window approach, dividing the EMG signal into overlapping windows of 1024 samples with a step size of 512. For consistency across datasets, we standardize all signals to 2000 Hz sampling rate through resampling when necessary. Recessary.Regarding channel configuration, we standardize all inputs to 8 channels to maintain architectural consistency and computational efficiency. For multi-channel EMG recordings that exceed 8 channels, we perform channel selection during preprocessing, retaining the 8 most informative channels based on signal quality metrics such as signal-to- ratio and muscle activation patterns. This selective approach ensures we capture the most relevant EMG information while maintaining a uniform input dimension that balances representational capacity with computational efficiency. Finally, the processed data is saved in HDF5 format, enabling efficient storage and incremental updates. This structured approach not only simplifies the preprocessing pipeline but also allows the wavelet-basse -bsed model to adaptively learn optimal signal processing strategies directly from the data, potentially discovering more effective filtering and feature extraction methods than traditional fixed preprocessing techniques.
NinaPro DB6 The NinaPro DB6 dataset is designed to aid the scientific community in studying the repeatability of sEMG classification for hand grasps. It includes data from 10 intact subjects who performed 7 different hand grasps, each repeated 12 times across 5 days. The data was collected using 14 wireless Trigno EMG electrodes, with a sampling rate of kHz. The dataset provides synchronized sEMG signals along with inertial measurements, capturing the movements of the forearm during hand grasp activities. The main goal is to develop models that can accurately recognize and classify these grasps for prosthetic control [50]
NinaPro DB7 The NinaPro DB7 dataset includes both myoelectric and inertial measurements from 20 intact subjects and 2 amputees. The data were collected using 12 active wireless Trigno EMG sensors along with 9-axis inertial measurement units. The subjects were asked to perform 40 different movements, which included basic finger and wrist movements as well as grasping tasks. The data were sampled at kHz, with additional kinematic data collected from a 18-DOF Cyberglove worn on the contralateral hand. This dataset provides valuable insights for prosthetic hand control, enabling improved motion recognition through both myoelectric and inertial data (11.
NinaPro DBS The NinaPro DB8 dataset is focused on the estimation and reconstruction of finger movements, rather than motion or grip classification. It includes data from 10 intact subjects and 2 right-hand transradial amputee participants. The dataset contains sEMG signals, accelerometer, gyroscope, and magnetometer data collected from 16 wireless Trigno EMG sensors, along with data from Cyberglove worn on the contralateral hand. The subjects were asked repeat different movements, with each movement lasting between 6 to 9 seconds, followed by a 3-second rest period. This dataset is specifically designed benchmark algorithms that decode finger position from contralateral EMG measurements using regression algorithms, making it valuable resource for prosthetic hand control and rehabilitation studies (52]
EMG2Pose The EMG2Pose dataset consists of surface electromyography (sEMG) recordings paired with ground-truth motion- capture recordings of hand movements. It contains 25,253 HDF5 files, each representing -mmealigned sEMG and joint angles for a single hand during single stage. The dataset spans 193 participants across 370 hours of data and includes 29 stages, with each stage lasting around one minute. The dataset structured with metadata that includes anonymized user IDs, session information, hand side (left or right), and whether the user was held out from the training set. This dataset is designed to advance the field of hand pose estimation from sEMG signals, offering a foundation for model training and evaluation in pose tracking and gesture recognition tasks (53).
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EMG2Qwerty The EMG2Qwerty dataset consists of surface electromyography (sEMG) recordings obtained while users perform touch typing on QWERTY keyboard. It contains 1,136 session files, recorded from 108 users over 346 hours, with each session file including left and right sEMG signal data, prompted text, and keylogger ground-truth data. The dataset is provided in an HDF5 format and offers a programmatic interface for easy access. It is particularly useful for training and testing models aimed at decoding keypresses or character-level recognition from sEMG signals, with benchmarks for both generic and personalized user models. This dataset is essential for research in sEMG-based text entry systems, focusing on enhancing accuracy virtual keyboard applications and prosthetics [54].
E Downstream Datasets Description
Physio Wave-ecg is tested on the three ECG corpora (PTB-XL [1). Chapman Chapman-Shaoxing [55], and CPSC 2018 [56]), while ThysioWave-emm is assessed on the three EMG gesture datasets (Ninapro DB5 [7], EPN-612 (20]. and UCI EMG-Gesture [58) Multi-modal generalization is examined
with DEAP and MPDB 337.
Table 17: Public datasets used for downstream evaluation.
Dataset
Domain
Subjects
Channels
fs (Hz)
Task
Ninapro DB5
EMG
10
16
200
Hand gestures
EPN-612
EMG
612
8
200
Hand gestures
UCI EMG-Gesture
EMG
36
8
200
Hand gestures
PTB-XL
ECG
18 869
12
500
Arrhythmia
Chapman-Shaoxa
ECG
45 152
12
500
Arrhythmia
CPSC 2018
ECG
10 330
12
500
Arrhythmia
DEAP
BEG/EMG
32
40
128
Emotion recognition
MPDB
EEG/ECG/EMG
35
64
1000
Driving behaviour
E.1
Downstream datasets for Physio Wave-ecg
PTB-XL The PTB-XL dataset an extended version of the PTB database, containing 21,837 12-lead ECG recordings from 18,884 patients. The dataset includes variety of cardiac conditions, with annotations for more than 60 different disease categories, including arrhythmias, myocardial infarction, and other heart diseases. It was recorded using standard 12-lead ECG systems, with signals sampled at 1000 Hz. PTB-XL is widely used for ECG classification tasks and provides a comprehensive resource for developing and testing machine learning models in cardiovascular disease detection and prediction. The dataset is publicly available and is often used as benchmark for evaluating ECG classification algorithms [9
Chapman-Shaoxing The Chapman-Shaoxing Shaoxing dataset is large-scale ECG dataset containing 11,000 12-lead ECG recordings from over 5,000 subjects. It includes data from patients with various cardiovascular conditions, such as arrhythmias, ischemic heart disease, and healthy individuals. The recordings were collected using standard ECG machines, with sampling rate of 500 Hz. This dataset is used in studies focused on ECG diagnosis and abnormality detection, offering rich source of data for training and evaluating models that aim to predict cardiovascular diseases. The dataset particularly valuable for advancing research in automatic ECG analysis and arrhythmia classification (55)
CPSC 2018 The CPSC 2018 dataset, used in the China Physiological Signal Challenge, contains 15,000 12-lead ECG recordings from 5,000 patients, spanning a wide range of cardiovascular conditions. The recordings are sampled at 500 Hz and are annotated with clinical information about the presence of various heart diseases. The dataset is designed to evaluate machine learning algorithms for ECG classification, particularly for arrhythmia detection. The CPSC 2018 dataset provides diverse and comprehensive set of ECG recordings, making a critical resource for developing and benchmarking automatic ECG analysis systems. It has been widely used ECG research and challenges to push forward the development of reliable and accurate diagnostic tools [56).
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E.2 Downstream datasets for PhysioWave-ee Wave-emg
NinaPro DB5 The NinaPro DB5 dataset includes surface electromyography (sEMG) recordings for hand gesture recognition. It consists of 40 different hand gestures performed by 40 subjects (24 male, 16 female). Each subject performed each gesture 10 times, resulting in a total of 400 gesture recordings per subject. The dataset is recorded using bipolar sEMG electrodes placed on the forearm, with a sampling rate of 2 kHz. The data is valuable for the development and evaluation of machine learning models for gesture recognition in prosthetics and a well-annotated set of hand gestures that can be used to train models for real-time sEMG-based gesture classification [57].
interaction, providing
EPN-612 The EPN-612 dataset consists of surface electromyography (sEMG) data collected from 612 subjects using the Myo armband. It contains 5 different hand gestures (wave-in, wave-out, pinch, open, and fist) and relaxed hand gesture, with 50 recordings per gesture from each subject. The dataset includes both raw sEMG signals and the corresponding labels for each gesture, making suitable for gesture recognition tasks. The data is widely used for training and evaluating machine learning models focused on real-time gesture recognition, with applications in prosthetics and assistive technologies. The dataset is publicly available for research and benchmark purposes [20]
UCI EMG-Gesture The UCI EMG-Gesture dataset contains sEMG recordings for hand gesture recognition, collected from 10 subjects. Each subject performed 10 different gestures, with repetitions for each gesture. The dataset was recorded using a 16-channel sEMG setup, with sampling frequency kHz. It includes labeled data for each gesture, making it suitable for training and evaluating gesture recognition algorithms. This data set is valuable for researchers developing gesture models [58].
E.3 Downstream datasets for multi-modal tasks
DEAP The DEAP (Dataset for Emotion Analysis using Physiological Signals) dataset is multi- modal dataset designed for emotion recognition research. It contains 32 participants, each providing data across multiple physiological signals, including ERG, EMG, and GSR, while watching music videos designed to evoke different emotional states. The dataset includes 40 one-minute-long video clips, with each participant providing emotional ratings on several dimensions, such as valence, arousal, dominance, and liking. The DEAP dataset is widely used for training and evaluating models for emotion recognition, particularly in applications like affective computing and human-computer interaction. The dataset is available for research and provides an essential resource for studies combining physiological signals and emotion analysis (3
The MPDB (Multimodal Physiological Dataset for Driving Behavior Analysis) is a com- prehensive dataset designed to analyze driver behavior using multimodal physiological signals. It contains data from 35 participants, including both male and female drivers, recorded during simu- lated driving tasks. The dataset includes 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye-tracking data. These signals were collected during five distinct driving behaviors: smooth driving. acceleration, deceleration, lane changing, and turning. The data was synchronized with a six-degree-of of-frredoooom driving simulator, providing a realistic driving experience. This dataset is valuable for studying driver cognition, decision-making, and the relationship between physiological responses and driving behavior, especially in the context of autonomous driving re- search. The dataset's 's multimodal nature allows for the exploration of advancet models that integrate physiological signals for more accurate behavioral predictions and safety system designs [37].
MPDB
F
VISUALIZATION
F1
Mathematical Properties Validation
We conducted a comprehensive mathematical analysis of the learned wavelet kernels extracted from our PhysioWave model, examining 10 filter pairs (5 wavelet bases x 2 filters) across multiple decom- position levels. Our analysis reveals that the model has successfully developed task-adaptive signal decomposition kernels that prioritize empirical performance over strict mathematical constraints.
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Analysis
hig
3.8
Noma lax Time Ngh
0.1
00
Figure 6: Learned wavelet kernel shapes from PhysioWave model. The upper panel shows all 10 learned filters (5 low-pass and 5 high-pass) with vertical offsets for clarity, Lower panels display individual filter characteristics, demonstrating smooth low-pass filters for signal approximation and oscillatory high-pass filters for detail extraction.
F111 Key Mathematical Properties
Energy Conservation: As shown in Figure 6 all learned filters demonstrate perfect unit energy normalization (100% of filters with ||w||2 I.0), ensuring stable signal decomposition and prevent- ing amplitude drift during multi-level analysis. This property is crucial to maintain the integrity of the signal at all decomposition levels(59).
Compact Support: The filters exhibit excellent spatial localization with average support lengths of 15.6 samples within the 16 16-sample window (97.5% utilization), demonstrating efficient use of the receptive field without boundary artifacts. This compact support enables precise temporal localization of EMG events(60)
Frequency Selectivity: Clear frequency differentiation emerged through training (Figure
Low-pass filters: Frequency centroids at 6.0-7.1 (normalized) with broader bandwidths (5.6-6.4), optimized for capturing EMG envelopes and removing high-frequency noise.
High-pass filters: Higher frequency centroids at 7.7-7.8 with narrower bandwidths (3.0- 5.3), specialized for detecting rapid muscle activation transients.
F.1.2 Task-Specific Adaptations
Our analysis reveals that the learned kernels have evolved beyond traditional wavelet constraints to become physiologically-inoonee strict mathematical properties (zero mean, vanishing moments), our filters demonstrate data-driven optimization that better captures EMG signal characteristies:
decomposition filters. While classical wavelets maintain
1. Adaptive Mean Values:
Non-zero mean values (0.017-0.149) 149) allow the filters to better track FMG baseline shifts and muscle fatigue patterns, which often present as DC components in real physiological recordings.
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Frequency Analysis Learned
Magritute Spedium
Phase Spectium
Phone
05
-2
Norma
horma
Energy Distntutionn
Frequency Chanaterretios
0.40
809
238
544
0:64
0
Trequeey Dn
Precummcy Gentrold Piommalood
Figure 7: Frequency domain analysis of learned wavelets. Magnitude spectrum showing clear frequency separation hetween low-pass and high-pass filters. (b) Phase spectrum demonstrating phase coherence. (c) Energy distribution heatmap across frequency bins. (d) Frequency characteristics scatter plot showing clustering of filter responses in physiologically relevant bands.
2. Specialized Frequency Response: The concentration of high-pass filter responses around 7.7-7.8 (normalized frequency) corresponds to the dominant frequency band of motor unit action potentials in EMG signals (typically 20-150 Hz), demonstrating automatic discovery of physiologically relevant frequency bands.
3. Heterogeneous Smoothness: Variable smoothness characteristics (Ist derivative std: 004- 0.465) reflect adaptation to the -tatitoti nature of EMG signals, with smoother filters for bascline tracking and sharper filters for spike detection.
F.1.3 Comparison with Standard Wavelets
As illustrated in Figure 8 the learned filters show minimal correlation (r 0.033-00100 104) with standard wavelet families (db4, db6, sym4, sym5, coif3). This divergence is not a limitation but rather evidence of successful adaptation to the unique characteristics of physiological signals. Traditional wavelets were designed for general signal processing, whereas our learned filters have automatically discovered decomposition patterns specifically optimized for EMG signal structures.
F.1.4 Empirical Validation
The departure from traditional wavelet properties represents a principled trade-off between mathe- matical elegance and empirical performance. Our learned filters achieve:
Superior classification accuracy (94.2% on EMG gesture recognition)
Enhanced noise robustness in elinical settings
Better generalization across subjects compared fixed wavelet bases
This shows that for biosignal processing applications, task-optimized learned filters outperform mathematically constrained traditional wavelets, validating approach of allowing the model
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Comparison Standard Wavelets
pm 04)
(m0.300)
04
102
-04
-88
Nerraeee
Nomaloed
00
-49
-08
Nerraioo 1mm
Homalodd
Figure 8: Comparison between learned filters and standard wavelets. Each subplot shows learned filter (blue solid line) overlaid with its most similar standard wavelet (red dashed line), with correlation coefficients (r) indicating the degree of similarity. The low correlation values (r 0.033-0. 104) demonstrate that our model has discovered novel, task-specific decomposition kernels distinct from traditional wavelet families.
to discover optimal decomposition kernels through end-to-end learning. The filters have effec- tively learned extract the most discriminative features from EMG signals while maintaining the multiresolution analysis framework essential for physiological signal processing.
F.2 Visualization of sEMG Pretraining
The following visualizations demonstrate the pretraining process for EMG signals, where the model leamss to reconstruct the masked regions of the input signal, specifically focusing on the restoration of multiscale wavelet decomposition features, Spec(X) (X). The images show different stages of reconstruction task, including the target signal, the prediction of the model, the masked areas, and the final reconstructed signal. These visualizations illustrate the model's ability to restore missing information from wavelet-decomppsed features [I].
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Figure 9: Reconstruction results for the 10th training epoch and the 1000th batch. The image illustrates the pretraining process where the model successfully restores masked regions of the signal during EMG pretraining. This represents the restoration of multi-scale wavelet-decomposed information an earlier training stage.
Figure 9 shows the results for the 10th training epoch and the 1000th batch of the EMG pretraining process. As seen in the image, the model has learned to effectively restore the missing parts of the signal, showcasing its ability handle the wavelet decomposition and its corresponding multiscale features.
West
Figure 10: Reconstruction results for the 20th training epoch and the 1000th batch. This image shows the pretraining process at later stage, where the model continues to restore missing information with increasing accuracy. The ability restore the wavelet decomposition this stage is further refined.
Figure disiplly the results for the 20th training epoch and the 1000th batch of the EMG pretraining process. At this stage, the model shows improved performance in reconstructing the masked regions, indicating its enhanced understanding of the signal's 's multiscale features and its ability to restore more complex signal components.
These visualizations highlight the progression of the model's learning ability during the pretraining process. The first figure 9 demonstrates the model's early-stage capability to restore missing multiscale features, while the second figure 10 shows the improvements made by the model as it continues training. This process emphasizes the model's ability to learn to restore wavelet- decomposed features, which critical for downstream applications such EMG signal analysis and classification.
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F33 Visualization of EPN612 Experimental Results
Confusion Matrix Test (Epoch Test)
nuGenture
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Actual
Birch
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MIVeOUt
pincc
Predicted
Figure 11: Confusion matrix on the EPN612 test set after final training.
Figure TT shows the test confusion matrix for EPN612 dataset. Per-class recalls remain consis- tently high (287.4%, typically >92%), with the highest recall achieved for noGesture (99.4%) Most misclassifications occur between semantically similar dynamic gestures: (a) open is occasion- ally confused with waveOut and pinch, and (b) pinch is sometimes predicted open. The sparse distribution off-diagonal entries indicates that the model has successfully learned discriminative representations for the remaining gesture classes.
Curves Test (Epoch
10-
08
Positive
reGesture (AUC 0.999)
wavsin IAUC 0.995)
waveOut (AUC 0.994)
(AUC 0.988)
open
fist
... Randam
Positive
Figure 12: ROC curves for all gesture classes on the test set after final training.
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Per-Class Metrics Test (Epoch Test)
3358 ses C3N2
Prediion
Percal
2 92 C3L
mie CARA
Score
044
1-12250 DHIoLaa
0-12253
In- C00L
Figure 13: Per-class precision, recall, and Fl-score metrics on the test set after final training.
Figure 12 presents the ROC curves for all six gesture classes the set. All models demonstrate excellent discriminative performance with AUC values ranging from 0.981 to 0.999. The noGesture class achieves the highest AUC of 0.999, followed by waveIn (0.995) and waveDut (0.994). The curves are positioned well above the random classifier diagonal, indicating strong separation between positive and negative classes across all gesture types.
Figure 13 shows the detailed per-class performance metrics. The noGesture class achieves near- perfect performance across all metrics (precision: 0.998, recall: 0.994, F1-score: 0.996). Dynamic gesture classes show consistently high performance, with Fi-scores ranging from 0.883 (open) 0.956 (waveIn). The open gesture exhibits the lowest recall (0.874) among all classes, while maintaining reasonable precision (0.892), suggesting some difficulty in detecting this particular gesture pattern.
Embeddinngs
Figure 14: 3D PCA embeddings visualization from multiple viewing angles showing feature repre- sentations learned by the Wavele(Transformer model.
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3D TSNE Emibeddings- M MMltiple Views
Wea Elecation 22, do
Wee Straaiio SV: eoth
211
9
Elocat 30" Altru 225
49
Figure 15: 3D t-SNE embeddings visualization from multiple viewing angles demonstrating non- linear clustering patterns in the learned feature space.
3D UMAP Embeddings Muttiple
Vow Elovation 22" muth
Vin Doveter 40" Admutt se*
14
LMAP
Boster Adtea
Elouation 30" Aarmuth 223'
Figure 16: 3D UMAP embeddings visualization from multiple viewing angles revealing the topologi- cal structure of gesture representations in the feature space.
Figures 14 15 and 16 present comprehensive 3D visualizations of the learned feature embeddings using three different dimensionality reduction techniques: Principal component analysis (PCA), t-distributed stochastic neighboring incorporation (t-SNE) and uniform manifold approximation and
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projection (UMAP), Each visualization shows the same feature space from four different viewing angles (elevation azimuth combinations of 20 /450, 45 /90", 60%/135? and 30%/2259) provide complete spatial understanding of the clustering patterns.
The PCA visualization (Figure 14p reveals the linear structure of the feature space, showing how gesture classes are distributed along the principal components of maximum variance. The noGesture class (red) forms distinct, compact cluster that well-separated from dynamic gesture classes, while the active gesture classes (waveIn, waveOut, pinch, open, fist) exhibit more distributed but still distinguishable patterns.
The visualization of t-SNE (Figure 15) emphasizes local neighborhood relationships and reveals non-linear clustering structures. The method successfully preserves local similarities, creating tight, well-separated clusters for each gesture class. In particular, the noGesture class forms a dense spherical cluster in the center, while dynamic gestures arranged in distinct regions around the periphery, suggesting strong discriminative power in the learned representations.
The visualization of UMAP (Figure 16) balances the preservation of local and global structures, revealing the topological organization of the feature space. UMAP shows excellent class separation with noGesture forming highly concentrated cluster, and dynamic gesture classes organized in a manifold like structure that preserves both local neighborhoods and global relationships between semantically related gestures.
Across the three visualization methods, several consistent patterns emerge: (1) the noGesture class demonstrates exceptional separability from active gestures, (2) dynamic gesture classes maintain distinct clustering while showing logical spatial relationships based on gesture similarity, and (3) the learned feature representations exhibit strong discriminative properties that facilitate effective classification. The multiview presentation confirms the robustness of these clustering patterns across different spatial perspectives.
E,4
Model Interpretability via GradCAM Analysis
To ensure clinical interpretability and validate that our model learns physiologically meaningful patterns, we employed Gradient-weighted Class Activation Mapping (GradCAM) [6]] visualize the temporal regions most influential to classification decisions. This analysis provides crucial insights into whether the model attends clinically relevant ECG segments corresponding established diagnostic criteria.
F4.1 Methodology
We applied GradCAM to the final transformer block of our trained PhysioWave-ECCC model, generat- ing importance heatmaps across the temporal dimension for representative samples from the Georgia ECG dataset. The analysis focused on cardiac rhythm classes with distinct temporal signatures, including Normal Sinus Rhythm (NSR), T-wave Inversion (TInv), and ST-segment Depression (STD). For each sample, we computed gradient-based importance scores and overlaid them on the original ECG signals identify regions of maximum model attention.
E.4.2 Temporal Attention Patterns
Figure 17 illustratts representative GradCAM visualizations for different cardiac conditions, revealing distinct attention patterns that align with clinical diagnostic features:
F.4.3 Clinical Validation of Learned Features
The GradCAM analysis revealed distinct attention patterns that strongly align with established clinical diagnostic criteria across different rhythm classes. For T-wave Inversion (Tinv), the model's attention was predominantly concentrated in the 0-400ms window, precisely targeting the early signal segments where T-wave morphology deviates from normal patterns. This focused attention confirms that the model successfully identifies repolarization abnormalities, a critical feature for diagnosing T-wave pathologies(62
In contrast, Normal Sinus Rhythm (NSR) samples exhibited distributed attention across periodic cardiac cycles with recurring importance spikes at regular intervals. This pattern indicates the model's
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(a) Normal Sinus Rhythm (NSR)
(b) T-wave Inversion (TInv)
(c) ST-segment Depression (STD)
(d) Complex Arrhythmia Pattern
Figure 17: GradCAM visualization results showing model attention patterns for different cardiac conditions. Red regions indicate high importance scores, demonstrating that the model focuses on clinically relevant temporal segments for each pathology.
reliance on complete P-QRS-T morphology rather than isolated peaks, demonstrating its ability to recognize the harmonious relationship between cardiac components that characterizes healthy rhythm patterns. The periodic nature of the attention maps further validates the model's understanding of rhythmic regularity 62].
For ST-segment depression (STD), the highest activation appeared consistently in the early temporal regions corresponding to post-QRS intervals, precisely where ST-segment changes manifest in clinical practice. This targeted attention demonstrates the successful isolation of diagnostically critical ST windows of the model, which is essential for detecting ischemic changes (63.. Complex arrhythmias presented variable attention patterns with multiple focal points distributed throughout the temporal sequence, reflecting the model's adaptive feature extraction capabilities when faced with irregular rhythm patterns and compound pathologies.
F.4.4 Quantitative Analysis of Attention Distribution
To quantify the focus of the model on clinically relevant intervals, we analyzed the temporal distribu- tion of GradCAM activation across 100 samples per class. The analysis revealed:
Pathology-specific localization: For ST-segment abnormalities, 78% of peak activations occurred within the 100-300ms post-QRS window, corresponding to the ST-segment interval in standard ECG interpretation.
Periodic attention for regular rhythms: NSR samples showed autocorrelation peaks in GradCAM maps at intervals matching the heart rate (mean R-R interval), confirming the model's recognition of rhythmic patterns.
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Early temporal focus for repolarization disorders: T-wave abnormalities triggered con- centrated attention in the first 40% of the signal window, aligning with the expected temporal location of T-wave morphology.
F.4.5 Clinical Implications
The GradCAM analysis provides three key insights for clinical deployment:
Diagnostic Transparency: The visualization enables cardiologists verify that automated diagnoses are based on appropriate ECG segments rather than spurious correlations or artifacts.
2. Trust Building: By demonstrating attention to the same temporal landmarks used in manual interpretation (P-waves, QRS complexes, STT-segmetss, T-waves), the model establishes credibility with clinical practitioners.
3. Educational Value: The attention maps serve as teaching tools, highlighting critical diagnostic regions for medical students and residents learning ECG interpretation.
These results confirm that our wavelet-based transformer architecture not only achieves high clas- sification accuracy but also learns interpretable, physiologically grounded features. The alignment between algorithmic attention patterns and established clinical knowledge validates the model's suitability for computer-aided diagnosis in real-world healthcare settings, where interpretability is paramount for adoption and regulatory approval.
G LIMITATIONS
While the proposed Physio Wave architecture represents significant advance in the processing of physiological signals, it is not without limitations. Our current approach focuses on large-scale models specifically trained for electromyography (EMG) and electrocardiography (ECG) signals, both of which have demonstrated state-of-the-ar performance in several downstream tasks. However, this approach currently limited to these modalities, and other important physiological signals, such as electroencephallggrphy (EEG) and photoplethysmography (PPG), remain underexplored.
Another key limitation lies in the fact that work focuses primarily separate models for different physiological signals. Although our unified framework integrates pre-trained EMG and ECG models with existing EEG encoder, the design not fully generalized across all biosignal modalities. This calls for further exploration into the development of universal, multimodal model capable of seamlessly processing wide variety of physiological signals. A model of this nature could function as comprehensive "physiological diagnostic" system, akin to a highly specialized medical expert, diagnosing and interpreting broad spectrum physiological signals across different conditions. Developing such a universal model would not only enhance the robustness of signal interpretation but would also streamline the diagnostic process across different sensor modalities.
We believe that addressing these gaps by expanding the range of modalities incorporated into our framework and further investigating the potential for unified, multimodal biosignal model will significantly improve the versatility and applicability of our approach real-world clinical and health monitoring settings. Future research could foeus training models for wider array of biosignals, as well as enhancing the multi-modal integration capabilities to create a truly universal model for physiological signal processing.
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Reviews:
[{"review_id": "ORsXYcCO6y", "reviewer": "Reviewer_jqy9", "summary": "Briefly 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "[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.", "was_revised": true, "final_justification": "Based on the originality of this paper, I decide to maintain my rate and recommend to add these experiments and clarifications to the revision.", "rebuttal": "Thank you for the detailed and constructive review. We answer your specific questions below.\n\n---\n# Q1 Ablating on Submodules\nWe added detailed ablation experiments on our designed submodules and make analysis on them. These experiments were performed by finetuning the pretrained small PhysioWave model while replacing specific submodules to evaluate their individual contributions.\n\n**Table 1: Main Ablation Study Results on EPN612 Dataset**\n| Configuration | SG | CAFFN | AW | Accuracy (%) | F1-Score (%) | Accuracy Improvement |\n|---------------|-----------|-------|------------------|--------------|----------|---------------------|\n| Baseline | ✗ | ✗ | ✗ | 89.88  | 89.76  |  - |\n| Only SG | ✓ | ✗ | ✗ | 91.71  | 91.66 | ↑1.83% |\n| Only CAFFN | ✗ | ✓ | ✗ | 91.53 | 91.46 | ↑1.65% |\n| Only AW | ✗ | ✗ | ✓ | 92.23 | 92.22 |  ↑2.35% |\n| CAFFN + AW | ✗ | ✓ | ✓ | 93.12  | 93.10 | ↑3.24% |\n| SG + AW | ✓ | ✗ | ✓ | 92.64  | 92.89 |  ↑2.76% |\n| SG + CAFFN | ✓ | ✓ | ✗ | 92.52 | 92.48 | ↑2.64% |\n| **Full Model** | ✓ | ✓ | ✓ | **93.85** | **93.51** | **↑3.97%** |\n\n*SG: Soft Gate, AW: Adaptive Wavelet (when AW is not used, a fixed db6 wavelet decomposition is applied)*\n\n**Individual Component Contributions**: Adaptive Wavelet shows the strongest individual impact (+2.35%), demonstrating the superiority of adaptive wavelet selection over fixed wavelet. Soft Gate provides selective feature enhancement (+1.83%), while CAFFN enables cross-scale feature fusion (+1.65%).\n\n**Component Synergy**: Two-component combinations reveal strong complementarity. CAFFN + AW achieves the best pairwise performance (+3.24%). SG + AW (+2.76%) and SG + CAFFN (+2.64%) show comparable contributions. The complete integration of all three components achieves optimal performance (93.85% accuracy, +3.97% improvement), demonstrating synergistic rather than redundant effects. \n\n**Table 2: Gate Mechanism Comparison on EPN612 Dataset**\n| Gate Type | Threshold | Accuracy (%) | F1-Score (%) | Accuracy Decrease vs SG |\n|-----------|-----------|--------------|--------------|-------------------------------|\n| Hard Gate | 0.3 | 92.07 | 92.05 | ↓1.78% |\n| Hard Gate | 0.5 | 92.25 | 92.23 | ↓1.60% |\n| Hard Gate | 0.7 | 92.17 | 92.19 | ↓1.68% |\n| Gumbel Gate | 0.5 | 91.47 | 91.51 | ↓2.38% |\n| **Soft Gate** | **-** | **93.85** | **93.51** | **-** |\n\n**Soft Gate**: Achieves optimal performance (93.85% accuracy) through continuous sigmoid-based gating that dynamically balances original signal components with wavelet-transformed features.\n\n**Hard Gate**: Shows performance degradation across all threshold values (92.07%-92.25% accuracy), with results being sensitive to threshold selection, demonstrating that binary decisions are suboptimal.\n\n**Gumbel Gate**: Exhibits the largest performance drop (↓2.38%), indicating that the stochastic sampling approach is less suitable for our physiological signal task compared to the soft gating mechanism.\n\n**Table 3: FFN Variant Comparison on EPN612 Dataset**\n| FFN Variant | Accuracy (%) | F1-Score (%) | Accuracy Decrease vs CrossScale CAFFN |\n|-------------|--------------|--------------|---------------------------------------|\n| Standard FFN | 91.95 | 91.93 | ↓1.90% |\n| CAFFN | 92.72 | 92.69 | ↓1.13% |\n| CrossScale No Attention | 93.29 | 93.31 | ↓0.56% |\n| **CrossScale CAFFN** | **93.85** | **93.51** | **-** |\n\n**Standard FFN**: The baseline two-layer feed-forward network achieves 91.95% accuracy, providing a solid foundation but lacking specialized mechanisms for multi-channel physiological signals.\n\n**CAFFN (Channel Aggregation FFN)**: Introduces depthwise convolution, decomposition operations, and element-wise scaling, improving accuracy to 92.72% (+0.77%). The channel aggregation mechanisms effectively handle multi-channel dependencies in signals.\n\n**CrossScale No Attention**: Extends CAFFN with simple cross-scale feature fusion using averaging and interpolation, achieving 93.29% accuracy (+1.34%). \n\n**CrossScale CAFFN**: The complete model incorporates multi-head attention for sophisticated cross-scale feature interaction, achieving optimal performance at 93.85% accuracy (+1.90%). The attention mechanism enables selective focus on relevant features from different decomposition levels, with a learnable scale parameter controlling feature integration.\n\n**Table 4: Wavelet Selection Ablation Study on EPN612 EMG Dataset**\n| Wavelet Configuration | Accuracy (%) | F1-Score (%) | Accuracy Improvement vs Random |\n|----------------------|--------------|--------------|-------------------------------|\n| Random Xavier | 92.25 | 92.22 | - |\n| db6 (Single) | 92.13 | 92.11 | ↓0.12% |\n| coif4 (Single) | 92.13 | 92.14 | ↓0.12% |\n| sym6 (Single) | 92.93 | 92.93 | ↑0.68% |\n| db6+sym6+coif4 | 93.51 | 93.51 | ↑1.26% |\n| **db4+db6+sym4** | **93.90** | **93.90** | **↑1.65%** |\n| db8+coif4+bior4.4 | 92.90 | 92.89 | ↑0.65% |\n\n**Table 5: Wavelet Selection Ablation Study on Georgia ECG Dataset**\n| Wavelet Configuration | Accuracy (%) | F1-Score (%) | Accuracy Improvement vs Random |\n|----------------------|--------------|--------------|-------------------------------|\n| Random Xavier | 62.38 | 48.75 | - |\n| db4 (Single) | 62.70 | 48.94 | ↑0.32% |\n| demy (Single) | 63.59 | 49.36 | ↑1.21% |\n| sym4 (Single) | 62.45 | 48.99 | ↑0.07% |\n| **db6+coif4+sym8** | **64.38** | **50.65** | **↑2.00%** |\n| db8+sym5+coif3+demy | 64.20 | 50.46 | ↑1.82% |\n| sym4+sym5+db6+coif3+bior4.4 | 63.70 | 49.93 | ↑1.32% |\n\n**Single vs Multi-Wavelet Performance**: Single wavelets show minimal improvements over random initialization (typically <1%), while multi-wavelet combinations achieve substantial gains: 1.65% for EMG and 2.00% for ECG. This demonstrates that the adaptive wavelet selector effectively leverages complementary characteristics from different wavelet bases.\n\n**Modality-Aware Design**: Optimal wavelet combinations differ between modalities. EMG signals benefit most from db4+db6+sym4, suited for transient muscle activation patterns, while ECG signals prefer db6+coif4+sym8, better capturing complex cardiac morphologies.\n\n---\n# Q2 Noise Filtering\n(1) We evaluated the denoising capability of our wavelet front-end especially soft gating by testing it with artificially corrupted biosignals. Clean multi-frequency synthetic signals were contaminated with Gaussian white noise at various SNR levels. Our denoising model consists of the wavelet decomposition front-end followed by a simple CNN reconstruction head to recover the clean signal. We evaluate denoising performance using Percentage Root-mean-square Difference which quantifies the reconstruction error between original clean signals and denoised outputs. Lower PRD values indicate superior noise reduction capability.\n\n**Table 6: Quantitative Results of Denoising**\n| SNR(dB) | PyWT | Wiener | Wavelet | Wavelet+SG |\n|---------|------|---------|---------|------------|\n| 30 |6.81% | 4.10% | 3.25% | **2.02%** |\n| 25 |7.96% | 6.58% | 5.61% | **4.19%** |\n| 20 |10.57% | 10.90% | 9.95% | **8.49%** |\n| 15 | 16.05% | 17.14% | 15.54% | **14.32%** |\n| 10 |27.58% | 30.57% | 26.38% | **25.04%** |\n\n**Wavelet Front-end with Soft Gating**: Our wavelet-based front-end demonstrates significant denoising capabilities, substantially outperforming traditional methods. The addition of soft gating further enhances noise reduction performance, with PRD reductions of 1.23% to 1.46% across different SNR conditions.\n\n(2) According to the seminal Donoho-Johnstone theorem on wavelet shrinkage[1], for signals belonging to a Besov function space $\\mathcal{B}_{s,q}^p$, the minimax optimal MSE convergence rate satisfies:\n\n$$\\text{MSE}_{\\text{optimal}} = O\\left(N^{-2s/(2s+1)}\\right)$$\n\nwhere $s$ denotes the smoothness parameter and $N$ is the sample size.\n\nAlthough SSL has proven successful in other domains, direct application to biosignals presents unique challenges due to distinct spectral structures. Our FgM approach achieves equivalent optimal convergence by performing frequency-domain operations fundamentally equivalent to wavelet shrinkage: adaptively preserving signal-dominated low frequencies while masking noise-dominated high frequencies. Under orthogonal transforms, this adaptive frequency selection achieves the bias-variance tradeoff necessary for minimax optimality, demonstrating that generic SSL approaches lacking spectral awareness are insufficient for biosignal processing. The expected squared error of our FgM estimator satisfies:\n$$\\mathbb{E}\\left[\\|f - \\hat{f}_{\\text{FGM}}\\|^2\\right] \\leq C \\cdot N^{-2s/(2s+1)}$$\nwhere $C$ is a constant related to the noise level.\n___\n# Q3 Multi-Modal Framework Adaptability\n(1) Based on our code architecture, the multi-modal framework supports dynamic modality combinations without retraining. The system implements dynamic HDF5 key detection to identify available modalities, conditionally initializes only the required frozen encoders, and adapts the fusion mechanism to dynamically weight available modalities using softmax-normalized learnable parameters.\n\n(2) This framework enables diverse clinical and research applications. Clinical uses include ICU monitoring, sleep diagnostics, and stroke rehabilitation, where multi-modal data provides earlier anomaly detection than single-sensor methods. Research applications include neuroscience and BCI studies examining brain-body coupling through synchronized physiological recordings. However, broader adoption is limited by the scarcity of large-scale, annotated multi-modal physiological datasets.\n\n(3) Latency and memory were profiled on an NVIDIA RTX 3070 GPU. The system achieves real-time performance with inference delays of 16-18 ms (single modality), 36.99 ms (dual modality), and 52.74 ms (full stack), supporting ≥20 FPS. The model footprint totals 135 MB with runtime GPU memory of 127-145 MB. Per-sample energy cost ranges from 1.2-5.0 J with 92 W power draw.\n\n---\n## References\n[1] Donoho D L. Ideal spatial adaptation by wavelet shrinkage. biometrika, 1994, 81(3): 425-455."}, {"review_id": "mKtRwbuRN0", "reviewer": "Reviewer_ofZV", "summary": "The 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).", "questions": "Question1: 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)?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\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 .", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the thorough review and insightful feedback. We address your comments and questions in detail below.\n___\n# Q1 Modality-Specific Adaptation for EEG and PPG Signals\n\nYou raise an important point regarding modality limitation. We have added experiments on both EEG and PPG signals, demonstrating that our PhysioWave model effectively adapts to these modalities and can extract meaningful features from their unique signal characteristics. These experiments validate the generalizability of our framework across diverse biosignals.\n\n## EEG\n\nAs you correctly identified, EEG signals present unique challenges including low SNR, high channel count, and heterogeneity. To validate our model's adaptability, we conducted one experiment on EEG dataset[1].\n\nOur original PhysioWave-EEG model chooses diverse wavelet bases (db4, db6, sym4, coif2, bior2.2) well-suited for EEG signal characteristics, with a robust transformer backbone (embed_dim=512, depth=8, num_heads=8) without modifying the core architecture. For the enhanced version, we made several targeted improvements to address EEG-specific characteristics:\n\n- **Multi-Scale Temporal Enhancement**: Added multi-scale convolutions to capture EEG's diverse frequency bands and improve SNR\n- **Channel Interaction Modeling**: Enhanced spatial relationship modeling between EEG electrodes  \n- **Window Attention**: Efficient processing of long EEG sequences with linear complexity\n\n**Table 1: EEG Sleep Stage Classification Results on Sleep-EDF Dataset**\n\n| **Method** | **Balanced Accuracy** | **Cohen's Kappa** | **Weighted F1** |\n|------------|----------------------|---------------|-----------------|\n| BENDR | 0.6655 | 0.6659| 0.7507 |\n| BIOT | 0.6622 | 0.6461 | 0.7415 |\n| LaBraM | 0.6771 | 0.6710 | 0.7592 |\n| EEGPT| 0.6917| 0.6857 | 0.7654 |\n| PhysioWave (original)| 0.6720 |0.6676 |0.7472|\n| **PhysioWave (enhanced)** | **0.7312** | **0.7206** | **0.7839** |\n\nAs shown in Table 1, even the original PhysioWave architecture demonstrates competitive performance against EEG-specific foundation models, validating its strong generalizability across biosignal modalities. Importantly, other baseline methods are finetuned from pretrained models, while our PhysioWave results are achieved through direct training from scratch, making our performance gains even more impressive. The enhanced version achieves **+3.9%** improvement over the best baseline (EEGPT) in Balanced Accuracy with substantial improvements across all metrics.\n\nAdditionally, we plan EEG pretraining to address heterogeneity and non-stationarity challenges. For the dimensionality issue, our proposed approach targets efficient compression after wavelet decomposition:\n\n- **Post-Wavelet Compression**: Applied immediately after multi-level wavelet decomposition to reduce the significantly expanded feature dimensionality from multiple decomposition levels across all channels\n- **Attention-Guided Compression**: Uses both channel and spatial attention to preserve the most informative multi-scale wavelet features while dramatically reducing dimensions\n\nThis planned strategy would maintain rich multi-scale wavelet information while keeping computation manageable for high-density electrode arrays, effectively addressing the dimensionality explosion that occurs after wavelet decomposition.\n\n## PPG\nWe evaluated PhysioWave on the PPG-DaLiA dataset. The dataset provides PPG signals collected during various physical activities. \n\n### Architecture Adaptations for Edge Deployment\n\nGiven the constraints of wearable and edge computing environments, we designed PhysioWave-PPG as a **compact variant** with several key optimizations. The architecture features reduced depth (4 transformer layers) and smaller embedding dimensions (192) to minimize computational overhead while preserving the core wavelet-transformer capabilities. We selected PPG-optimized wavelets (db4, coif2, bior2.2) specifically chosen for their effectiveness in capturing the periodic characteristics inherent in cardiovascular signals. \n\n**Table 2: Activity Recognition**\n\n| Method | Parameters | Accuracy | Precision | Recall | F1 Score | AUROC |\n|--------|------------|----------|-----------|---------|----------|-------|\n| **PhysioWave-PPG** | **2.53M** | **0.6209** | **0.6015** | **0.6077** | **0.5952** | **0.9420** |\n| Pulse-PPG [2] | 28.5M | 0.4234 | 0.4398 | 0.3663 | 0.3648 | 0.8246 |\n\n**Table 3: Heart Rate Regression**\n\n| Method | Parameters | MAE | MSE | MAPE |\n|--------|------------|-----|-----|------|\n| **PhysioWave-PPG** | 1.57M | 5.22 | 65.62 | 5.83% |\n| Pulse-PPG [2] | 28.5M | **3.705** | **59.81** | **4.84%** |\n| Deep PPG [3] | 8.5M | 7.65 |- | - |\n| NAS-PPG [4] | 0.8M | 6.02 | - | - |\n\n*For classification, we use a two-layer head with intermediate dimension of 384. For regression, we adopt a single layer head.*\n\n### Performance Analysis\n\nOur PhysioWave-PPG achieves exceptional performance across both tasks. For activity recognition, it delivers **+63.1% F1-score improvement** (0.5952 vs 0.3648) and **+14.2% AUROC improvement** (0.9420 vs 0.8246) while using **18.2× fewer parameters** (2.53M vs 28.5M) compared to the Pulse-PPG pretrained foundation model. For heart rate regression, our compact architecture (1.57M parameters) achieves competitive results without large-scale pretraining.\n\nWe do not plan pretraining for PPG signals due to their inherent characteristics: PPG data typically exhibits strong periodicity and regularity as you mentioned, making it well-suited for **direct training**. Additionally, PPG applications primarily target wearable devices where computational efficiency is paramount. Our results demonstrate that PhysioWave-PPG achieves excellent performance compared to much larger pretrained models, validating that our domain-optimized design effectively captures PPG's periodic patterns without requiring extensive pretraining infrastructure.\n___\n# Q2 Cross-Device Robustness and Hardware Compatibility\n\nYes, PhysioWave has been extensively tested across signals from diverse recording devices. The concern about consumer EMG bands vs. clinical ECG machines is not applicable here, as EMG and ECG modalities are pretrained and deployed separately using their respective specialized models. However, within the same modality, **recording devices can indeed vary significantly**. For example, our EMG evaluations span three public Myo-based datasets with different hardware configurations and electrode placements, yet the model performs consistently well, highlighting its device-level transferability:\n* Ninapro DB5: Recorded with two Thalmic Myo armbands  worn side-by-side on the forearm; the upper armband sits near the radio-humeral joint while the lower is tilted ~22.5° to fill inter-electrode gaps.\n* EMG-EPN-612: Using a single Myo armband on varying forearm locations.\n* UCI EMG-Gesture: Wearing a Myo Thalmic bracelet, with slight placement differences across users.\n\n**Sampling Rate Mismatch and Noise Handling Pipeline**: For sampling rate inconsistencies and device-specific noise, we implement a comprehensive preprocessing pipeline followed by our adaptive wavelet denoising:\n\n1. **Initial Preprocessing**: At the preprocessing stage for raw signals, we apply traditional signal processing techniques including Butterworth bandpass filtering to remove baseline drift and high-frequency artifacts, notch filtering to suppress power-line interference, and resample signals to a unified sampling rate via spline interpolation.\n\n2. **Adaptive Wavelet Denoising**: Our learnable wavelet decomposition module then performs secondary noise reduction through adaptive multi-scale filtering, automatically suppressing residual noise while preserving physiological features.\n___\n# Q3 Dynamic Multi-Modal Fusion \n\nOur fusion strategy already employs **dynamic, learnable, softmax‑normalized weights**,not static. Each pretrained encoder acts as a specialist whose “opinion” (logits) is combined through a set of fusion coefficients trained jointly with the lightweight classification heads. During training these coefficients adapt to the data, so at inference they automatically privilege the modality that proves most informative for a given sample, exactly as a medical team would defer to whichever specialist’s assessment is most relevant.\n\nWe have also experimented with a Q‑K‑V cross‑attention alignment layer that allows one modality to query another. In practice, this dynamic alignment performed worse than our current late‑fusion scheme, likely because (i) the physiological datasets available are still modest in size, making the extra parameters easy to overfit, (ii) the large sampling‑rate gaps (e.g., EEG 128 Hz vs. EMG 2000 Hz) left residual timestep misalignment as you mentioned. To improve, we will explore learnable resampling layers that map every modality onto a shared temporal grid before fusion in the future, \n\nIn addition, we acknowledge that multi-modal datasets like DEAP have **limited diversity**, which reflects the broader challenge of scarcity of large-scale, demographically diverse multi-modal physiological datasets. However, our multi-modal framework enables easy extension to new downstream datasets as they become available, facilitating efficient adaptation to diverse populations.\n\n---\n## References\n[1] Wang G, He Y, et al. Eegpt: Pretrained transformer for universal and reliable representation of eeg signals[J]. Advances in Neural Information Processing Systems, 2024, 37: 39249-39280.\n\n[2] Saha M, Mao W, et al. Pulse-ppg: An open-source field-trained ppg foundation model for wearable applications across lab and field settings[J]. arXiv preprint arXiv:2502.01108, 2025.\n\n[3] Reiss A, Schmidt P, et al. Deep PPG: Large-scale heart rate estimation with convolutional neural networks[J]. Sensors, 2019, 19(14): 3079.\n\n[4] Song S B, Kim J H. NAS-PPG: PPG-based heart rate estimation using neural architecture search[J]. IEEE Sensors Journal, 2021, 21(13): 14941-14949."}, {"review_id": "dEKltQ9TWE", "reviewer": "Reviewer_iAdX", "summary": "The 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.", "questions": "Can 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?", "limitations": "While 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.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths: 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.", "was_revised": true, "final_justification": "The authors' rebuttal successfully addressed all major concerns through comprehensive new experiments and analyses, warranting an upgrade from borderline accept to accept. The PPG extension experiments demonstrate exceptional efficiency and validate architectural adaptability across physiological modalities. The detailed interpretability analyses provide clinically valuable insights that bridge algorithmic performance with physiological understanding. Thorough computational benchmarking confirms real-time feasibility on edge devices with specific latency and power metrics essential for wearable applications. The comprehensive ethical framework appropriately addresses privacy and deployment concerns with structured safeguards. The work constitutes a solid contribution to biosignal processing that advances healthcare monitoring capabilities and merits acceptance.", "rebuttal": "We are grateful for your balanced review highlighting both our contributions and areas for improvement. We address your specific questions and limitations point by point below.\n___\n# Q1 Adapting PhysioWave for PPG Signals\n\nYou raise an important point regarding modality limitation. By introducing new experiments on PPG data, we demonstrate that PhysioWave’s capabilities extend beyond its original ECG and EMG‑based validation.\n\nWe evaluated PhysioWave on the PPG-DaLiA dataset, which provides PPG signals collected during various physical activities. \n\n### Architecture Adaptations for Edge Deployment\n\nGiven the constraints of wearable and edge computing environments, we designed PhysioWave-PPG as a **compact variant** with several key optimizations. The architecture features reduced depth (4 transformer layers) and smaller embedding dimensions (192) to minimize computational overhead while preserving the core wavelet-transformer capabilities. We selected PPG-optimized wavelets (db4, coif2, bior2.2) specifically chosen for their effectiveness in capturing the periodic characteristics inherent in cardiovascular signals.\n\n**Table 1: Activity Recognition**\n\n| Method | Parameters | Accuracy | Precision | Recall | F1 Score | AUROC |\n|--------|------------|----------|-----------|---------|----------|-------|\n| **PhysioWave-PPG** | **2.53M** | **0.6209** | **0.6015** | **0.6077** | **0.5952** | **0.9420** |\n| Pulse-PPG [1] | 28.5M | 0.4234 | 0.4398 | 0.3663 | 0.3648 | 0.8246 |\n\n**Table 2: Heart Rate Regression**\n\n| Method | Parameters | MAE | MSE | MAPE |\n|--------|------------|-----|-----|------|\n| **PhysioWave-PPG** | 1.57M | 5.22 | 65.62 | 5.83% |\n| Pulse-PPG [1] | 28.5M | **3.705** | **59.81** | **4.84%** |\n| Deep PPG [2] | 8.5M | 7.65 |- | - |\n| NAS-PPG [3] | 0.8M | 6.02 | - | - |\n\n*For classification, we use a two-layer head with intermediate dimension of 384. For regression, we adopt a single layer head.*\n\n### Performance Analysis\n\nOur PhysioWave-PPG achieves exceptional performance with remarkable efficiency. For activity recognition, it delivers **+63.1% F1-score improvement** (0.5952 vs 0.3648) and **+14.2% AUROC improvement** (0.9420 vs 0.8246) while using **18.2× fewer parameters** (2.53M vs 28.5M) compared to the Pulse-PPG foundation model. For heart rate regression, our compact architecture (1.57M parameters) achieves competitive results, demonstrating that our domain-optimized design effectively captures PPG's periodic patterns with minimal computational overhead, making it ideal for wearable device deployment.\n___\n# Q2 Interpretability of the Learned Wavelet Features\nWe acknowledge the importance of visualizing learned wavelet features and conducted comprehensive interpretability analyses to evaluate their alignment with established physiological markers. While submission format constraints prevent us from including figures directly, we provide detailed descriptions of the wavelet feature characteristics and their physiological significance below.\n\n## 3D t-SNE Embedding Analysis\n\nWe performed 3D t-SNE visualization on learned feature representations from the EPN612 EMG dataset to examine clustering patterns and their physiological significance. The visualization reveals clustering structures that align with established motor control principles:\n\n- **Baseline State**: The noGesture class forms a dense, central cluster representing the resting physiological state with minimal motor activity, serving as the neutral reference point for voluntary movements.\n\n- **Gesture Clustering**: Dynamic gesture classes (waveIn, waveOut, pinch, open, fist) arrange in distinct peripheral regions with physiologically meaningful relationships. WaveIn and waveOut gestures cluster adjacently due to shared oscillatory patterns and similar flexor-extensor muscle synergies. Pinch and open gestures form neighboring clusters, reflecting their complementary nature in fine motor control through antagonistic muscle activation.\n\n- **Motor Control Validation**: The embedding structure validates key neurophysiology principles. Gestures requiring similar muscle synergies cluster together, with clear separation between fine motor control (pinch/open) and gross motor movements (wave gestures), reflecting distinct neural pathways.\n\n\n## ECG GradCAM Analysis\n\nWe employed Gradient-weighted Class Activation Mapping (GradCAM) to interpret our model decision-making process on the Georgia ECG dataset. By analyzing gradient-based feature importance across temporal sequences, we validated the physiological relevance of learned representations and ensured clinical interpretability of automated diagnoses.\n\n**Table 3: GradCAM Analysis Results Across Cardiac Rhythm Classes**\n\n| Rhythm Class | GradCAM Pattern | Physiological Interpretation |\n|--------------|-----------------|------------------------------|\n| **T-wave Inversion (TInv)** | Heat concentrated in **0-400ms window**, precisely targeting early signal segments where T-wave abnormalities manifest. | Confirms the model attends to **repolarization abnormalities** rather than unrelated temporal segments, demonstrating pathology-specific feature detection. |\n| **Normal Sinus Rhythm (NSR)** | Distributed attention across **periodic cardiac cycles** with recurring importance spikes at regular intervals. | Indicates the model relies on **complete P-QRS-T morphology** rather than isolated peaks, matching normal rhythmic patterns. |\n| **ST-segment Depression (STD)** | Highest activation in **early temporal regions** corresponding to post-QRS intervals where ST-segment changes occur. | Shows the network successfully isolates **diagnostically critical ST windows** instead of generic baseline regions. |\n\nThis visualization provides cardiologists with clear insights into model reasoning, confirming that automated diagnoses are based on the **analogous temporal landmarks** that clinicians use for manual ECG interpretation[5]. This alignment between algorithmic attention and clinical expertise makes our approach suitable for computer-aided ECG diagnosis in clinical environments.\n___\n# Q3 Computational Profile\nWe conducted comprehensive benchmarking of our **PhysioWave small variant** across four real-world deployment scenarios: **real-time windows (100ms/200ms), batch inference (4×8×200), and long sequences (1×8×1000)**, with 100 iterations per scenario to ensure statistical reliability. Our testing methodology included precise latency measurements on current hardware platforms, with **power consumption estimates derived from FLOPS-based calculations** using established device efficiency profiles for edge computing platforms. **Results demonstrate excellent edge performance**: The PhysioWave small variant, with only **4.8M parameters (18.4MB) and 0.24 GFLOPS**, achieves **inference latencies of 15-26ms** while maintaining 95th percentile performance under 32ms. The model sustains **44-60 FPS throughput** with modest memory footprint (52-402MB GPU) and **estimated power consumption ranging from 0.24mW on GPU to 244mW on Raspberry Pi 4**. This lightweight computational profile confirms our model is ideally suited for real-time physiological signal processing on edge devices.\n___\n# Q4 Ethical Considerations and Privacy Protection\n\nFirst, we clarify that our manuscript focuses exclusively on **physiological signal processing for healthcare applications**. While you mention psychiatric relevance, our work does not claim psychiatric applications, which would require different validation frameworks and ethical considerations. Although our experiments include the DEAP dataset with EEG signals, we frame this as general physiological signal processing rather than psychiatric research.\n\n**Data Privacy and Access Control**: All datasets in our study implement stringent access controls requiring researchers to sign End User License Agreements(EULA) before access through formal request procedures. Our data processing strictly adheres to GDPR compliance with comprehensive anonymization to eliminate personally identifiable information.\n\n**Specific Deployment Safeguards**: \n- **Clinical Supervision Requirement**: Real-time inference is restricted to **licensed medical institutions** with mandatory supervision by qualified physicians or rehabilitation specialists.\n- **Model Transparency**: We will release a comprehensive **model card** disclosing data lineage, applicable patient populations, known limitations, and safety warnings.\n- **Controlled Access Framework**: We plan to open-source inference code and training scripts, while model weights will be available through research applications requiring signed data-use agreements.\n\n**Healthcare Setting Safeguards**: Beyond supervised deployment, we mandate ethical training for healthcare practitioners and continuous monitoring mechanisms to prevent unintended surveillance applications. These measures ensure PhysioWave contributes to clinical advancement while maintaining rigorous standards for privacy protection, algorithmic fairness, and ethical compliance.\n\n---\n\n## References\n[1] Saha M, Xu M A, Mao W, et al. Pulse-ppg: An open-source field-trained ppg foundation model for wearable applications across lab and field settings[J]. arXiv preprint arXiv:2502.01108, 2025.\n\n[2] Reiss A, Indlekofer I, Schmidt P, et al. Deep PPG: Large-scale heart rate estimation with convolutional neural networks[J]. Sensors, 2019, 19(14): 3079.\n\n[3] Song S B, Nam J W, Kim J H. NAS-PPG: PPG-based heart rate estimation using neural architecture search[J]. IEEE Sensors Journal, 2021, 21(13): 14941-14949.\n\n[4] Koelstra S, Muhl C, Soleymani M, et al. Deap: A database for emotion analysis; using physiological signals[J]. IEEE transactions on affective computing, 2011, 3(1): 18-31.\n\n[5]van de Leur R R, et al. Discovering and visualizing disease-specific electrocardiogram features using deep learning. Circulation: Arrhythmia and Electrophysiology, 2021, 14(2): e009056."}, {"review_id": "O91JoHEd8b", "reviewer": "Reviewer_nhy9", "summary": "the 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.", "questions": "Analysis 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.", "limitations": "The 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?,", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "The 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.", "was_revised": false, "final_justification": "", "rebuttal": "We are deeply grateful for your comprehensive and highly professional review, which provides invaluable technical insights and constructive feedback that has substantially improved the quality and impact of our research.We address the weaknesses you identified and answer your specific questions systematically below.\n___\n# Q1 Analysis of the Learned Wavelet\n\n## Learned Wavelet Filter Bank Analysis\n\nWe first conducted comprehensive ablation studies to demonstrate how our adaptive wavelet selection mechanism learns **domain-specific** filter combinations that outperform both random initialization and single wavelet approaches. As mentioned in our paper, we select different wavelet bases for different modalities, as each signal type has its most suitable wavelets for optimal processing characteristics.\n\n**Table 1: Wavelet Selection Ablation Study on EPN612 Dataset (EMG)**\n\n| Wavelet Configuration | Accuracy (%) | F1-Score (%) | Accuracy Improvement vs Random |\n|----------------------|--------------|--------------|-------------------------------|\n| Random Xavier | 92.25 | 92.22 | - |\n| db6 (Single) | 92.13 | 92.11 | ↓0.12% |\n| coif4 (Single) | 92.13 | 92.14 | ↓0.12% |\n| sym6 (Single) | 92.93 | 92.93 | ↑0.68% |\n| db6+sym6+coif4 | 93.51 | 93.51 | ↑1.26% |\n| **db4+db6+sym4** | **93.90** | **93.90** | **↑1.65%** |\n| db8+coif4+bior4.4 | 92.90 | 92.89 | ↑0.65% |\n\n**Table 2: Wavelet Selection Ablation Study on Georgia 12-Lead ECG Dataset**\n\n| Wavelet Configuration | Accuracy (%) | F1-Score (%) | Accuracy Improvement vs Random |\n|----------------------|--------------|--------------|-------------------------------|\n| Random Xavier | 62.38 | 48.75 | - |\n| db4 (Single) | 62.70 | 48.94 | ↑0.32% |\n| demy (Single) | 63.59 | 49.36 | ↑1.21% |\n| sym4 (Single) | 62.45 | 48.99 | ↑0.07% |\n| **db6+coif4+sym8** | **64.38** | **50.65** | **↑2.00%** |\n| db8+sym5+coif3+demy | 64.20 | 50.46 | ↑1.82% |\n\n## Analysis and Discussion\n\n**Single vs Multi-Wavelet Performance**: Single wavelets show minimal improvements over random initialization (typically <1%), while multi-wavelet combinations achieve substantial gains: **1.65% for EMG** and **2.00% for ECG**. This demonstrates that our adaptive wavelet selector effectively leverages complementary characteristics from different wavelet bases.\n\n**Modality-Aware Design**: Optimal wavelet combinations differ between signal types. EMG signals benefit most from **db4+db6+sym4**, suited for transient muscle activation patterns, while ECG signals prefer **db6+coif4+sym8**, better capturing complex cardiac morphologies.\n\n**Prior Knowledge Value**: Wavelet initialization provides valuable domain-specific prior knowledge, with the adaptive selector learning to weight different wavelets based on input characteristics, combining expert knowledge with data-driven optimization for superior physiological signal classification.\n\n**User Accessibility**: The minimal difference between **random initialization** and single wavelets suggests that even users unfamiliar with signal processing and wavelet analysis can achieve excellent results using our PhysioWave framework, as the adaptive mechanism automatically learns optimal filter combinations without requiring domain expertise.\n\n## Mathematical Properties Validation\n\n**Visualization Clarification**: We acknowledge the importance of visualizing learned wavelet shapes and thoroughly describe their characteristics below. While submission format constraints prevent direct figure inclusion, we provide comprehensive quantitative analysis of the learned filter properties.\n\n**Experimental Approach**: We conducted an extensive analysis on **30 learned wavelet filters** extracted from our pretrained PhysioWave-EMG model across 3 decomposition levels. Each filter kernel has a size of 16 samples (shape: (8, 1, 16)), comprising low-pass and high-pass filters essential for multi-resolution analysis.\n\n**Results and Observations**:\n\n**1. Orthogonality**: Essential complementary low-high pass filter pairs demonstrated **reasonable orthogonality with inner product values typically ranging from 0.005 to 0.075**. While perfect orthogonality was not achieved across all filter combinations, critical orthogonal relationships necessary for effective signal decomposition and reconstruction were maintained, particularly between paired low-pass and high-pass filters within each decomposition level[1].\n\n**2. Vanishing Moments**: The learned filters achieved **near-zero mean values (ranging from 0.011 to 0.054)**, partially satisfying admissibility conditions. However, **formal vanishing moment analysis reveals zero vanishing moments for all 30 filters**, indicating these function as specialized, task-optimized feature extractors rather than traditional wavelets with polynomial suppression capabilities[2].\n\n**3. Compact Support**: All filters demonstrated excellent **localized energy distribution within the 16-sample window, with support ratios between 0.832 and 1.000**, indicating effective energy concentration without significant boundary artifacts.\n\n**4. Smoothness**: Clear frequency-based differentiation emerged between filter types. **Low-pass filters exhibited lower smoothness variations (1st derivative: 0.104–0.235; 2nd derivative: 0.078–0.207)** with bell-shaped characteristics, while **high-pass filters showed higher variations (1st derivative: 0.269–0.465; 2nd derivative: 0.333–0.887)** with pronounced oscillatory patterns. This heterogeneous smoothness reflects task-specific adaptation to EMG signal characteristics[3].\n\n**Discussion of Learned Filter Shapes**:\n- **Low-pass Filters**: Displayed **smooth, bell-shaped profiles with frequency centroids between 5.85-7.04**, resembling classical wavelets (most similar to db1 and sym4). These are optimized for signal approximation and noise reduction.\n- **High-pass Filters**: Exhibited **sharp oscillatory patterns with frequency centroids between 7.80-7.90**, designed for extracting high-frequency details.\n\n**Conclusion**: Our analysis demonstrates that the learned filters exhibit **essential decomposition properties** including selective orthogonality, compact support, and frequency-appropriate smoothness characteristics. However, with zero vanishing moments, these represent **task and modality-specialized filter banks** rather than traditional wavelets. This adaptation creates an **effective learned signal decomposition specifically optimized for biosignals**, functioning as **adaptive wavelet feature extractors** enhanced through end-to-end learning rather than classical wavelet transforms. Traditional wavelets were designed for general signal processing, but physiological signals have unique characteristics such as EMG bursts, ECG morphology variations, and EEG artifacts that benefit from adaptive, task-specific decomposition rather than mathematically constrained transforms[4]. Our approach represents a paradigm shift from theoretically pure to empirically optimized signal processing, delivering superior performance for healthcare applications.\n___\n# Q2 Frequency-Guided Masking and Noise Processing Consistency\n\nWe appreciate your question and recognize there may be a terminological misunderstanding regarding our frequency-guided masking implementation.\n\nThe \"noise\" in our frequency-guided masking does not refer to external signal noise that would require frequency filtering. Instead, it refers to **computationally generated random values from a uniform distribution (0 to 1)** used as a mathematical regularization tool. These random values introduce **controlled stochasticity to prevent purely frequency-based masking**, ensuring that masking decisions maintain appropriate randomness while respecting spectral characteristics.\n\n**Mathematical Implementation**: The masking strategy combines frequency importance scores with uniform random values:\n\n$$\\\\text{combined\\\\_score} = (1-\\\\alpha) \\\\times \\\\text{random\\\\_uniform}(0,1) + \\\\alpha \\\\times \\\\text{frequency\\\\_importance}$$\n\nwhere $\\\\alpha$ is the **importance ratio parameter** controlling the balance between frequency-guided selection and randomness. **These combined scores are then used to rank all patches, with patches receiving lower combined scores being preserved** (typically low-frequency important information), while higher-scoring patches (often high-frequency) are more likely to be masked. This **hybrid approach prevents deterministic masking of all high-frequency components** by introducing appropriate randomness, improving model generalization.\n\nThis approach is fundamentally different from adding external noise to signals, which would indeed raise the frequency consistency concerns you highlighted. We will clarify this terminology in our revised manuscript to distinguish between computational randomness and physical noise sources.\n\n---\n\n## References\n[1] Mallat, S. “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation”, IEEE TPAMI, 1989\n\n[2] Daubechies, I. “Orthonormal Bases of Compactly Supported Wavelets”, Comm. Pure Appl. Math., 1988, p. 911 ff.\n\n[3] Mallat, S. “Singularity Detection and Processing with Wavelets”, IEEE TIT, 1992\n\n[4] Rafiee J, Rafiee M A, Prause N, et al. Wavelet basis functions in biomedical signal processing[J]. Expert systems with Applications, 2011, 38(5): 6190-6201."}]

Author rebuttal:

Meta-review: The paper introduces a wavelet-Transformer framework for self-supervised pretraining on physiological signals (primarily EMG and ECG). A learnable wavelet decomposition produces multi-scale tokens, and a frequency-guided masking scheme trains the model. The pretrained encoders support downstream tasks and a simple multimodal fusion setup.

Reviewers agree the method is technically sound and practically useful for non-stationary, low-SNR biosignals. The learnable wavelet module and frequency-guided masking are well motivated, and the experiments are broad, with strong results on several ECG/EMG benchmarks and gains in multimodal settings. Multiple reviews rate the paper accept, citing originality, clear writing, and potential impact on healthcare applications.

One reviewer questions novelty relative to prior self-supervised work and notes that current ablations are not sufficient to attribute gains to specific sub-modules. After rebuttal, the reviewer maintained a borderline-reject mainly due to limited ablations and presentation gaps; others remained positive.

The majority of reviews are positive with accept ratings, citing consistent empirical gains and a clear, well argued design. The remaining concerns are addressable with added analysis and reporting, not fundamental flaws. Weighing the strength of results and practicality against the missing ablations and analysis, I recommend accept.
Decision: Accept (poster)