Benchmarking ECG FMs: A Reality Check Across Clinical Tasks
Authors: M A Al-Masud
Juan Lopez Alcaraz
Nils Strodthoff
Venue: ICLR 2026 Poster (2026)
Keywords: ECG
foundation model
benchmark
representation learning
Primary area: foundation or frontier models, including LLMs
TL;DR: We provide a comprehensive benchmark for ECG foundation models

Abstract: The 12-lead electrocardiogram (ECG) is a long-standing diagnostic tool. Yet machine learning for ECG interpretation remains fragmented, often limited to narrow tasks or datasets. FMs promise broader adaptability, but fundamental questions remain: Which architectures generalize best? How do models scale with limited labels? What explains performance differences across model families? We benchmarked eight ECG FMs on 26 clinically relevant tasks using 12 public datasets comprising 1,650 regression and classification targets. Models were evaluated under fine-tuning and frozen settings, with scaling analyses across dataset sizes. Results show heterogeneous performance across domains: in adult ECG interpretation, three FMs consistently outperformed strong supervised baselines. In contrast, ECG-CPC, a compact structured state-space model, dominated 5 of 7 task categories, demonstrating that architecture matters more than scale. FMs improved label efficiency 3.3-9× over supervised baselines, though scaling behaviors varied across architectures. Representation analysis reveals that models with similar performance learn markedly different internal structures, suggesting multiple viable paths to effective ECG representation. Overall, while FMs show promise for adult ECG analysis, substantial gaps remain in cardiac structure, outcome prediction, and patient characterization. ECG-CPC's strong performance despite being orders of magnitude smaller challenges the assumption that FM quality requires massive scale, highlighting architectural inductive biases as an untapped opportunity.

Published as conference paper ICLR 2026
BENCHMARKING ECG FMs: A REALITY CHECK ACROSS CLINICAL TASKS
MA A AI-Masud, Juan Miguel Lopez Alcaraz Nils Strodthoff Al4Health Division, Carl von Ossietzky Universität Oldenburg Oldenburg, Lower Saxony, Germany {m. al -asuud, juan. lopez www.pz.alcaaae nils. wtw.sssttttoooooooffolooo.cocc de
ABSTRACT
The 12-lead electrocardiogram (ECG) is long-standing diagnostic tool. Yet machine learning for ECG interpretation remains fragmented, often limited to narrow tasks or datasets. FMs promise broader adaptability, but fundamental questions remain: Which architectures generalize best? How do models scale with limited labels? What explains performance differences across model families? We benchmarked eight ECG FMs on 26 clinically relevant tasks using 12 public datasets comprising 1,650 regression and classifi- cation targets. Models were evaluated under fine-tuning and frozen settings, with scaling analyses across dataset sizes. Results show heterogeneous performance across domains: in adult ECG interpretation, three FMs consistently outperformed strong supervised base- lines. In contrast, ECG-CPC, compact structured state-space model, dominated 5 of 7 task categories, demonstrating that architecture matters more than scale. FMs improved label efficiency 3.3-9x over supervised baselines, though scaling behaviors varied across architectures. Representation analysis reveals that models with similar performance learn markedly different internal structures, suggesting multiple viable paths effective ECG representation. Overall, while FMs show promise for adult ECG analysis, substantial gaps remain in cardiac structure, outcome prediction, and patient characterization. ECG-CPC's strong performance despite being orders of magnitude smaller challenges the assumption that FM quality requires massive scale, highlighting architectural inductive biases an untapped opportunity.
1 INTRODUCTION
Clinical relevance Electrocardiography (ECG) is a widely used non-invasive tool for assessing cardiac
function and systemic physiology (Siontis et al. 202I). Accurate interpretation essential for detecting
myocardial infarctions (Strodthoff al. 2020). evaluating cardiovascular risk (Bhatia & Dorian 2018)).
and guiding clinical decisions (Rokos et 2010). ECG features also reflect systemic conditions such as
electrolyte imbalances (Diercks et al.) 2004), metabolic disorders (Wald) (2066), and physiological factors
(Lopez Alcaraz et al., [2025), broadening its clinical utility. The growing availability of large-scale ECG datasets (Wagner al. 2022 Gow al., 20233; Ribeiro et al. [222I) has made machine learning increas-
ingly pivotal for automated interpretation, enabling population-scaa scale screening (Kalmady et al. 2024) and
supporting clinical workflows (Graf et al. 2024).
Promise of FMs for ECG We follow the original definition from the Stanford Institute for Artificial Intelligence (Bommasani al.) 2022 and define foundational models (FMs) "models trained
"A14Health Division https:///ua de/ed///nnn /e//////// 4health

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Models
Datasets
Insights
FMs (Transformer)
33088
Architecture over model size
HuBERT-ECG
Negit
2404
7TDAL
113W
efficiency
ST-MEM ECG-FM
Chairan
1045
1397
Representation analysis
FMMs [C NNN
P1
Task C Categories
ECGFN ECGFounder MERL
Aduit Interpretation ECG diag. (Class.)
Cardlac
ICD-10 discharge diag. (Class.)
Adult ECG
Ieland
Supervised Baseline
54 (SSM)
Pediatic ECG interpretation ECG diag. (Class.)
Non-cardiac outcomes ICD-10 discharge diag. (Class.)
Sex (Class.)
Net1D (CNN)
Age (Reg.)
Biametrics (Reg.)
(Reg.)
Lab values IR eg.)
Proposed M)
Cardlac structure function Echocardiogram (Class.)
Acute care prediction Deterioration (Class.) Mortality
Vital signs (Re
ECG-CPC
Stuters
ICU admission (Class.)
Figure 1: Overview of the benchmarking pipeline for ECG FMs.
on broad data (generally using self-supervision at scale) that can be adapted to wide range of downstream tasks". FMs have transformed fields such as natural language processing (Myers et al. (024) and computer vision (Awais al., 2025). where large-scale scale pretraining produces robust and transferable representations across tasks and domains (Subramanian et al.) (023). FMs now also emerge in biomedical domains, includ- ing pathology (Chen et al.J (202)), retinal imaging (Zhou et al., 2023), and biomedical time series, where ECG serves as a natural testhed given its ubiquity and clinical relevance. Clinical FMs offer three key ad- vantages: (i) higher predictive performance than training from scratch, (ii) improved label efficiency via pretrained representations, and (iii) the utility of FMs as frozen feature extractors for downstream tasks. These benefits could enable stronger predictive models, support rare-disease studies on limited data, and guide the choice of FMs for specific applications.
Research gap Despite their promise, ECG FMs remain inadequately evaluated. Prior studies compare FMs against weak baselines (Li et al.
2025 Kim 2024 Na et al., 2024 Liu al. 2024), and existing benchmarks focus on narrow datasets or single l task categories, preventing generalizable conclusions about model capabilities (Na et al. 2024 Liu et al. 2024). Critically, fundamental questions remain unanswered: Do larger FMs consistently outperform smaller, task-specific models? Which architectural choices from Transformers, CNNs, state-space models yield the transferable representations? How do different pretraining strategies affect label efficiency across clinical domains? Without systematic comparison under controlled conditions, the field cannot distinguish genuine advances from benchmark overfitting favorable task selection.
Contributions of this work This work addresses three research questions central ECG FM develop- ment: Architecture: Which backbone architectures generalize best across diverse ECG tasks?; Efficiency: How do FMs scale with labeled data compared to supervised baselines?; Representation: What explains performance differences between architecturally distinct models?
We present a comprehensive benchmark covering 8 FMs, 12 datasets, and 26 clinical tasks across clas- sification and regression. Our key findings are: (1) Architecture matters more than scale. Structured state-space models (SSMs) outperform Transformer-bast models on 5 of 7 task categories despite being
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much smaller. We introduce ECG-CPC, a lightweight SSM trained with minimal resources, which matches exceeds all evaluated models, challenging the assumption that FM quality scales primarily with parameter count. (2) FMs improve label efficiency 3.3-9x. Scaling analysis shows strong models substantially reduce labeled data requirements compared to supervised baselines, especially for outcome prediction, though gains vary across architectures, guiding model selection under data constraints.
(3) Similar performance arises from divergent representations. Centered kernel alignment (CKA) (Kornblith et al.) 2019;; Nguyen et al.) analysis reveals that models with comparable downstream performance learn markedly different internal feature structures, suggesting multiple viable paths effective ECG representation and raising questions about whether task performance alone suffices for evaluating FMs.
2 BACKGROUND
AT-enhanced ECG Machine learning and deep learning underpin automated ECG interpretation, im- proving arrhythmia detection, risk stratification, and clinical decision support. Models of different architec- tural flavors, from CNNs to RNNs Transformers, trained through supervised learning, have been shown to achieve strong performance. However, they require large, curated datasets and often generalize poorly across populations, devices, and settings (Ribeiro al. 2020 Hannun et al. 2019; Hong et al. 2020). Benchmarking robust supervised baselines across multiple datasets remains crucial for assessing the bene- fits of representation learning and pretraining (Strodthoff al. 2020) Nonaka Seita 2021] Hong al.,
2020
ECG FMs Inspired by the success of FMs in language and vision, several approaches have been proposed for ECG. Architectures include CNNs (mainly ResNet variants), transformers (often with convolutional en- coders), and structured state-space models. Pretraining methods vary from supervised and weakly supervised to contrastive and non-contrastive self-supervision. Pretraining datasets also differ widely, from Computing
Cardiology 2021 challenge subsets (Reyna et al. 2021b) to MIMIC-IV-ECG (Gow et al. 2023 to the
large-scale HEEDB dataset (Koscova al. 2024).
Table 1: Summary of the eight ECG FMs and two supervised baselines evaluated in this work, including their backbone architectures, training paradigms, pretraining datasets and sizes, and parameter sizes. Pretraining datasets: a: HEEDB; b: Chapman, Ningbo, CODE-15%: c: MIMIC-IV-ECG; d: CODE, PTB, CPSC2016 8, CPSC-Extra, PTB-XI., Georgia, Chapman, Ningbo, Hefei, SPH, MIMIC-IV-ECG; CPSC2018, CPSC- Extra, PTB-XL, Georgia, Ningbo, Chapman, MIMIC-IVECG; indicates models that were trained in this work, all other models were taken from the literature.
Name
Backbone
Pretraining
Pretraining Samples Parameters
ECGFounder
CNN
Supervised
10.7M
33.8M
ECG-JEPA
Transformer
JEPA
174k
87.2M
ST-MEM
Transformer
Masked Autoencoder
174k
90.3M
MERL
CNN
Weak sup., contrastive
800k
4.6M
-CGFM-KED
CNN
Weak sup.. contrastive
800k
9.7M
HoBERT-ECG
Transformer
MLM
9.
97.2M
ECG-FM
Transformer
1.5M
93.9M
ECG-CPC
SSM
CPC
10.7M
3.8M
CNN
33.8M
S4*
SSM
2.2M
Benchmarking FMs The benchmarking of FMs has a long tradition in other fields such as computer vision (Goldblum et al.. 2023 and NLP (Chang al. 2024). In the medical domain, most efforts have far
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focused on medical imaging (Neidlinger et al., EEG FMs have been put forward (Xiong et al. 2025 for ECG FMs exists.
2025
Lee et al. 2025). Recently, benchmarking results for
However, no sengesscale, comprehensive benchmark
3 METHODS
3.1 MODELS
Table summarizes the investigated models, including backbones, pretraining methods, and datasets, and parameter counts. As custom pretraining is infeasible, only FMs with publicly available pretrained weights are included and integrated via wrapper modules into a common evaluation framework. We benchmark eight FMs and two supervised baseline models as described below. Minor discrepancies in parameter counts stem from adjusting the classification head to each dataset's label count, and for multimodal models we only the signal encoder, focusing on ECG signals rather than full released models with text other modalities; note that these encoders retain an implicit advantage from having been pretrained alongside additional modalities.
FMs We consider diverse architectures and pretraining strategies. ECGFounder (Li et al. 2025) uses a RegNettinnppied CNN pretrained on HEEDB with a supervised loss. ECG-JEPA (im 2024) employs a transformer-based joint embedding predictive architecture (JEPA) (Assran et al. [203)] We use the multi-
block variant. ST-MEM (Na al. (0244) uses a VDT-ID transformer trained as a masked autoencoder (He)
et al.) (022). MERL (Liu et al. (024) applies contrastive text-signal alignment (Radford et al. (201)). with
the ResNet18 8 variant evaluated. ECGFM-KED (Tian al., 2024) uses ResNet backbone and minimizes a contrastive loss between signals and ECG report text. HuBERT-ECG and ECG-FM (McKeen et al. 2025
are transformer-based. pretrained via masked language modeling, with ECG-FM additionally incorporating a sequence-level contrastive loss. Finally, we pretrain structured state-space (SSM) model on HEEDB
using contrastive predictive coding (CPC) (van den Oord et al. 2018 Mchari & Strodthoff] Supplementary Material).
2023 (sce
Supervised baselines We evaluate two backbones trained from scratch: Net1D, from ECGFounder (Li et al., 2025), and S4 (Mehari & Strodthoff
2023), structured state-space model known for its ability to capture
long-range dependencies (Gu et al. 2022). S4 has proven to be a strong baseline on PTB-XI. and other
datasets (Mehari & Strodthofi] 2023: Strodthoff et al. 2024).
3.2 DATASETS AND BENCHMARKING TASKS
We group the benchmark datasets into seven categories based on the qualitative nature of the underlying labels. See Table 2 in the Supplementary Material for details sample sizes and task characteristics. Adult/pediatric ECG interpretation: These two categories, the most studied in the literature, involve predicting diagnostic ECG statements from cardiologists. Benchmarks include PTB (Bousseljot et al. (1955, Ningbo (Reyna et al. 2022). CPSC2018 and CPSC-Extra (Reyna et al. 202 2022). Georgia (Reyna et al. SPH (Liu al. 2022a b), CODE- 15% (Ribeiro al. 2020 A separate category includes ZZU pECG (Tan et al. 2025 Jian et al. (025) for pediatric ECG interpretation. Cardiae structure and function: This category
2021a 2022), Chapman (Reyna al.,
202 la
2022).
2021 and PTB-XL (Wagner et al. 2022
predicts outcomes from complementary modalities, here echocardiography. We use the EchoNext dataset (Blias& & Finer 2025), which is used to capture cardiac structure and function. Cardiac and non-cardiac
These categories cover the prediction of cardiac and non-cardiac discharge diagnoses from the first emergency department ECG (Strodthoff et al. (024).. distinguishing cardiac (ICD-10 chapter I) and non-cardiac diagnoses. Acute care predictions: ECG is a key modality for acute decisions. We predict clinical deterioration, mortality at multiple time frames, and intensive care unit (ICU) admission (Alcaraz et al. 2025), training joint model for cardiac, non-cardiac, and acute care outcomes to computational
outcomes:
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resources. Patient characteristics: This category includes tasks where ECGs predict con-diagnostic patient characteristics, such as sex, age, biometrics, ECG features, laboratory values (Alcaraz. & Strodthoff, 2025), and vital signs (Gow et al.) (023). The datasets span small specialized cohorts large population studies, covering diverse classification and regression tasks. We train a single model for all tasks in this category using combined classification and regression losses.
3.3 METHODOLOGY
Finetuning/lnnaar evaluation Pretrained encoders augmented with a linear head matching each down- stream task. All models use the standard 12 ECG leads without additional resampling or filtering. Training optimizes binary cross-entropy (classification)
MAE (regression) with AdamW (learning rate x 10-3, weight decay 1 x 10 "), batch size 64, for 100 epochs, with model selection on the validation set via macro AUROC or MAE. To mitigate scale interference in multi-target regression, targets are z-normalized using training-set statistics. During fine-tuning, we apply layer-dependent learning rates: model backbones are split into two parts whose learning rates are reduced by factors of 100 and 10 relative to the prediction head (see Table 34 for ablation).
soaih-hormalization statistics are frozen for linear and frozen evalua- tion. For frozen evaluation, the linear head is replaced with a learnable query-attention head (Bardes et al.) 2024), which uses single trainable query vector to attend over encoder tokens and produce a pooled rep- resentation, providing a lightweight, parameter-efficien pooling mechanism for frozen embeddings. When supported by the architecture, use 2.5-sccond ECG segments instead of full 10-second recordings, longer windows substantially increase compute with minimal performance gain. At inference, predictions are averaged across four non-overlapping 2.5-second segments, improving performance over single-segment evaluation. These choices follow established discriminative ECG practice (Mehari & Strodthoff (023). In line with modern robustness recommendations, we do fix global random seed.
Evaluation For classification, we report macro-averaged AUROC as the primary measure of overall discrim- inative performance. For regression, we report mean absolute error (MAE) averaged across z-normalized predictions and targets. Per-label AUROC and MAE values are provided in the Supplementary Material. Statistical uncertainty is estimated via empirical bootstrapping on the test set (n 1,000). Pairwise model comparisons are conducted by bootstrapping performance differences; differences whose 95% confidence interval excludes zero are deemed significant. Rankings are derived from these tests, with ties indicating no significant difference, thereby reflecting both relative performance and statistical uncertainty across task categories. We adopt macro AUROC as the primary metric because it the standard threshold-free ranking measure of discriminative ability. For clinical deployment, threshold-dependent metrics (e.g.. sensitivity and specificity at label-specifis operating points) are also relevant; an exploratory consistency analysis of rank- ings under such metries is provided in Appendix A.10 Moreover, recent theoretical and empirical evidence (MDDermott et al. 2024) indicates that AUROC is generally more reliable than alternatives such as AUPRC, particularly under label imbalance. Zero-shot evaluation is excluded because most ECG foundation models in our benchmark lack semantic label supervision, making zero-shot predictions neither meaningful nor com- parable across architectures. Instead, consistent with current best practice for medical time-series FMs and the definition of foundation models in |Bommasani et al. (2022), we focus on linear probing, frozen-feature evaluation, and full fine-tuning, which more reliably assess representation quality and adaptability.
4 RESULTS
Results overview Full finetuning, frozen evaluation, and linear evaluation results are given in Tables B. 4. and 5 (Supplementary Material). Tables indicate statistically significant differences compared to the respective best method this task. Ranked lists for all tasks and models are provided in Table We further summarize this by reporting median ranks across all tasks of given category in Figure 2 Appendix A.4
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presents comparative predictions for the 10 best-performing conditions per task, illustrating both prediction quality and label diversity.
4.1 SUPERVISED BASELINES
Supervised baseline vs. literature Our supervised baseline performs on par with or surpasses the literature results. The S4-based model achieves AUROCs of 0.941 (vs. 0.9417 (Mehari & Strodthoff (023)) on PTB-XI, 0.908 (vs. 0.843 (Strodthoff et al. 2024)) for cardiac discharge, 0.849 (vs. 0.764 (Strodthoff et al.) 2 for non-cardiac discharge, 0.863 (vs. 0.752 (Alcaraz et al. 2025)) for clinical deterioration, 0.747 (vs.
0.746 (Alcaraz al. ))25)) for ICU admission, and 0.874 (vs. 0.816 (Alcaraz et al.
2025 for mortality.
Improvements on MiMiC-based tasks (except for ICU) are attributed to the multi-task training objective.
Impact of model architecture We also compare the supervised S4 model with the convolutional Net backbone used ECGFounder, excluding supervised transformers due to their poor performance. Table 3 reports numerical results, and Table provides statistically significant rankings: S4 ranks first across tasks, while NetlD typically four or more positions lower. Although not our primary focus, this backbone comparison reinforces prior findings that CNNs are suboptimal for physiological time series (Strodthoff ct al. 2024
4.2 FINETUNING
Adult ECG interpretation: Across 11 tasks on 9 datasets. ECGFounder, ECG-JEPA, and ECG-CPC are the top performers, often statistically surpassing the S4 baseline. ECG-FM ranks fourth overall, occasionally matching the leaders but underperforming on Georgia, Chapman, and PTB-XL, while MERL, ST-MEM, HuBERT-ECG, and ECGFM-KED generally fail outperform S4. Pediatric ECG interpretation: ECG- JEPA leads, followed by ECGFounder, ST-MEM, MERL, ECG-CPC, and S4, despite the absence of pe- diatric pretraining data. Cardiac structure function: ECG-CPC ranks first for echocardiography pre- dictions, followed by ECGFounder, ECG-JEPA, ST-MEM, MERL, and S4. Cardiac and non-cardiac outcomes: ECG-CPC dominates, matching ECG-FM on non-cardiac and S4 on cardiac outcomes. ECG- Founder performs relatively poorly. likely due to limited overlap between pretraining and target diagnostic labels. Acute care predictions: ECG-CPC and ECG-FM perform best across three tasks, followed by ECGFounder, ECG-JEPA, and MERL,, but none significantly exceed S4. Patient characteristics: ECG- CPC ranks first in 5 of tasks, surpassing S4 in 3; MERL and ECG-FM generally match or slightly trail S4, whereas ECGFounder and ECG-JEPA typically fall below it.
4.3 FROZEN AND LINEAR EVALUATION
Frozen vs. supervised Model rankings under frozen evaluation largely mirror finetuning results, with some differences. ECGFounder and ECG-JEPA continue to perform strongly on adult ECG interpretation, match- ing the supervised baseline, while ECG-CPC ranks slightly lower, with median rank of 3. ECG-JEPA still dominates pediatric ECG interpretation. In other categories. ECG-CPC continues to lead. Notably, ECGFounder and ECG-JEPA can serve as effective frozen feature extractors for adult ECG interpretation tasks, achieving supervised-level performance. In the remaining categories, this applies to selected tasks for ECG-CPC under frozen evaluation and for ST-MEM under linear evaluation.
Finetuning frozen/linear Model rankings under finetuning largely mirror frozen/linear evaluation. Strong models like ECG-JEPA, ECGFounder, and especially ECG-CPC maintain strong performance across finetuning
other evaluation modes. However, some models such MERL, and ECG-FM rely reach competitive rankings, Interestingly, ST-MEM shows a much better relative ranking under linear eval- uation than finetuning. This highlights that finetuning and linear/frozen evaluation relate to largely similar,
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though not completely congruent, aspects of representational quality and should therefore both be consid- ered.
Fiseraszo
- Frizes
ECGFounder
ECG-JEPA
ST-MEM
ECG-CPC
Acyte
Arat
ECG-FM
Add
NO
Figure Radar plots summarizing model performance ranks (lwwer rank indicates statistically significantly better performance) for the eight FMs across the 7 investigated tasks. Our investigated ranking criteria accounts for confidence interval overlaps within each of the tasks and datasets. The plot is based data from Table 7
Frozen vs. linear Most methods are only slightly affected when using linear instead of non-linear pre- diction head. Deviations from this pattern are ST-MEM, which ranks among the best FMs under linear evaluation unlike in frozen evaluation, and ECG-CPC, which consistently underperforms with a linear head. The latter effect likely relates to pretraining: supervised or global contrastive objectives encourage discrimi- native pooled representations, unlike purely token-level pretraining. These results can be seen incentive to combine token- and sequence-leve objectives, as done in ECG-FM and also commonly observed in com- puter vision (Caron al. 202I). However, advocate frozen evaluation as less biased measure of FM representational quality than finetuning.
4.4 LABEL EFFICIENCY
Label efficiency Scaling curves for three FMs are shown in Figure 3 with scaling parameters listed in Table 30) All models stay below the supervised baseline (S4) throughout the entire range of considered training set sizes. We use the parametric form of the fits to work out a label efficiency ratio N /N,N, i.e.,
the fraction of samples N required for the pretrained model to reach the same performance as the supervised baseline for given N. For N in the range of 250 1000, this yields label efficiency ratios between 0.30- 0.62 for ECGFounder, 0. 10-442 for ECG-JEPA and 0.21-0.40 for ECG-CPC, see Table for details. This positions ECG-JEPA as the most label-efficient model, in particular the very low sample size regime, closcly followed by ECG-CPC. This establishes the label efficiency for FMs. Furthermore, the results show that ECG FMs fulfill the promise of an improved label efficiency not only in comparison to the respective model architecture trained from scratch but also against strong supervised baseline, improving label efficiency by up to a factor of 9. Beyond absolute label efficiency, scaling analysis distinguishes between data-efficiency slope and performance ceiling. ECG-JEPA learns
relevant benchmark parameter
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1 ---arauuoOOO (Error Rate)
Setup To isolate the effect of dataset size from task diffi- culty, we perform a controlled scaling experiment for the cardiac structure & function prediction on EchoNext (in- corporating only labels with 10,000 counts). Training and validation subsets are subsampled in powers of 2 down to 1/128, using multi-label stratification (Wagner et al. 2022) on diagnostic labels, age bins, and sex. We finetune ECG- Founder, ECG-JEPA, and ECG-CPC these subsets, com- paring pretrained models to training from scratch, with the S4 supervised baseline reference. Performance plot- ted as - macro AUROC, and scaling curves are fitted as CN-a + Lop where C' is constant, N is the size of the training set, the scaling exponent, and Lo the residual er- ror.
ECGFounder ECG JEPA ECG-CPC
x
x0-1
10a 10a Number of Training Samples
Figure 3: Scaling with dataset size on EchoNext across the best-performing FMs and supervised baseline.
quickly with few samples (steep slope) but plateaus at a lower accuracy than ECG-CPC, which improves slowly yet reaches higher ceiling. Thus, FMs differ in both how fast and how far they improve as data increases, suggesting that model choice should weigh slope versus ceiling depending on whether rapid low-label learning or maximal final performance the priority. For the case of cardiac structure analysis, this establishes ECG-JEPA as superior model in the small sample regime (< 10 training samples) and ECG-CPC as superior model in the large sample regime, thereby providing an actionable recommendation for practitioners.
4.5 REPRESENTATION SIMILARITY ANALYSIS
We analyze intra-model CKA patterns for FMs their original, unfinetuned form (Figure 4. ECG-CPC shows a progression from redundant early CNN layers distinct S4 layers, with the final S4 block most specialized. ECGFounder's convolutional stem is dissimilar to higher layers, while mid-network layers (SO- S4) are highly similar, with specialization emerging only in the final layers. In ECG-JEPA, the embedding layer is distinct, but intermediate transformer blocks (Blk1-BIk1)) are nearly identical, with only the final block specialized.
1.5
0.8
0.5
0.6
0.5
(a) ECG-CPC
(b) ECGFounder
(c) ECG-JEPA
(d) ST-MEM
Figure 4: Intra-model layer-wise representation similarity analysis. CKA heatmaps comparing all internal layers within each of the best performing FM on PTB-XL (all) dataset. Higher values (yellow) indicate similar representations between layers. CKA computed using Gaussian RBF kernel (a = 1.0) on 2,500 samples per model.
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ST-MEM exhibits similar patter, with mostly homogeneous mid-section and specialized final layers, while the first transformer block stands out as dissimilar to neighboring blocks indicating a possible archi- tectural bottleneck. Overall, CKA highlights that ECG-CPC exhibits the clearest and most structured evolu- tion of representations, while other architectures show mid-layer redundancy or homogeneous transformer blocks. For more detailed discussion including inter-model representation comparisons across depths, see Section A.9 in the supplementary material.
4.6 MODEL EFFICIENCY
For practical deployment, we benchmark the investigated models across GFLOP counts, GPU memory us- age, and inference efficiency. Although SSM-based models are not as computationally efficient as CNNs terms of inference efficiency. SMM (built on CPC) remains several times more efficient than transformer- based approaches across FLOPs, memory, and inference speed. More importantly, the efficiency gap be- tween CNNs and SSMs becomes less critical when considering model quality: SMM delivers markedly stronger predictive performance than both CNNs and transformers, offering a balanced trade-off between computational cost and superior representation quality-making it an attractive choice for practical, real- world ECG deployment. The full results are given in Table Table332in in the supplementary material.
5 DISCUSSION
Details matter Reliable downstream performance depends on careful tuning. Layer-dependent learning rates consistently improve results, and some models (e.g.. HuBERT-ECG, ECG-FM) fail to train at all without them. As detailed in Table (34] this benefit is strongly architecuure-dependenn, with transformer and SSM-based models showing marked improvements with layer-dependent learning rates. Among CNN- based models, ECGFounder remains invariant, ECGFM-KED degrades, while MERL shows modest im- provements. Adjustable input size (where possible) also matters: using 2.5-second crops with test-time averaging outperforms the full 10 seconds inputs.
Comparable assessment ECGFounder, ECG-JEPA, and ECG-CPC are strong on adult ECG interpretation, sometimes surpassing the supervised baseline. Pediatric ECG is dominated by ECG-JEPA. Other categories are led by ECG-CPC, followed by ECG-FM, while ECGFounder and ECG-JEPA are weaker here. Several models fall clearly below the supervised baseline, showing that self-supervised pretraining does not nec- essarily yield effective downstream performance. No single model consistently excels across all tasks and evaluation modes, though ECG-CPC comes closest this goal with finetuning. This underscores that FM se- lection for downstream tasks should be informed by benchmarking results and similarity of the downstream task the task categories considered here.
Pretraining strategies This benchmark cannot definitively determine the optimal pretraining method, as downstream performance reflects the interaction of pretraining strategy, dataset, and model architecture [T. Superior supervised performance may favor SSMs over CNNs, while transformers align naturally with masking used in self-supervised methods. Larger datasets can help (ECGFounder, ECG-CPC) but are not sufficient (e.g., HuBERT-ECG), and large-scale models do not automatically generalize. ECG-CPC's strong performance outside ECG interpretation suggests self-supervised pretraining can be advantageous, though its unidirectional backbone limits supervised performance relative to bidirectional models. These results underscore the need for like-for-like pretraining comparisons on a common dataset (e.g.. HEEDB) with standardized architectures. Fully disentangling dataset, architecture, and training strategy remains future work; our benchmark reveals practical differences, but controlled unified-dataset ablations are needed explain them.
Model architecture our benchmark, we provide the first systematic evidence that compact SSMs outper- form much larger Transformers and comparable CNNs time-series tasks despite having far fewer parame-
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ters, raising the theoretical question: what properties of SSM representations enable such efficient learning minimal capacity? These models carry strong inductive biases, stable long-range memory, smooth spectral filtering, and globally parameterized convolutions, that align exceptionally well with the structure of ECG signals, enabling efficient learning even at small scale. This empirical insight, provides grounding that can catalyze new theoretical work.
S4 among SSMs Although S4 is an earlier state-space model, it remains strong and widely used SSM baseline for continuous clinical time series due to its stable memory, spectral filtering, and computational efficiency, all of which align well with physiological signal structure. Prior ECG/EEG studies (Mchari & Strodtthoff] 2023 Wang & Strodthoff, 2025) and recent SSM surveys (Somvanshi et al. 2025| Patro
& Aggneswaaan 2025), , consistent with internal experiments shown in Appendix [A.]] show that newer variants such as Mamba often excel in discrete domains but do not consistently outperform S4 on continuous medical signals, supporting its inclusion as a competitive and representative SSM baseline.
Model complexity The evaluated models differ markedly in size, computational efficiency, and predictive performance. Parameter counts increase from SSMs (smallest) to CNNs to transformers (largest). In terms of practical efficiency, considering GFLOPs, GPU memory, and inference latency, CNNs are fastest, followed by SSMs, with transformers being the least efficient. Importantly, SSM-based models, despite moderate computational cost, achieve the highest predictive performance, outperforming both transformers and CNNs, which show comparable accuracy.
Model insights Frozen and linear evaluation reveal the nature of learned representations and the implicit knowledge captured by different FMs. Insights from probing, as used here, remain coarse. but methods from explainable Al such as analyzing representation structures and their alignment across layers and models (Vielhaben et al. 2023) could provide more detailed insights into the knowledge acquired by these models.
Limitations This work has several limitations. First, the proposed tasks include only in-distribution tests; out-of-distribution evaluation would require additional datasets with compatible labels. While diagnostic label mismatches make this challenging, it is feasible for demographic data. Second, multi-task models generally improve classification performance, but for regression we applied z-normalization to mitigate is- sues. Still, the multi-task model used for computational efficiency may underperform single-task models on certain tasks. Third, the consistent benefit layer-dependent learning rates during finetuning indicates potential for improved finetuning strategies. Fourth, since models are pretrained different datasets, iso- lating which factors drive performance differences is difficult. Retraining on a unified dataset would enable cleaner comparisons but computationally prohibitive. Following standard practice in vision and NLP, we prioritize practical utility by evaluating models as released; controlled unified-dataset comparisons remain important future work.
6 CONCLUSION
In this work, we present a comprehensive benchmark of ECG foundation models (FMs) across seven task categories. Performance varied across domains: ECGFounder, ECG-JEPA, and ECG-CPC excelled in adult ECG interpretation, while ECG-CPC outperformed others in categories where many FMs failed to match the supervised baseline. Overall, selected ECG FMs show promise, outperforming strong supervised baselines during finetuning and achieving comparable performance when used as frozen feature extractors. ECG- CPC, introduced here, is a small-scale model based on structured state-space backbone, trained single NVIDIA L40 GPU for three weeks. Its strong benchmark performance highlights opportunities for further improving ECG FMs. Code and model weights publicly released. LLMs were used only for language refinement. Our ECG-CPC framework and weights are available at httptttt//// //gi hub. com / AT 4Hal wereallthooooo thUot/ fm non-bbennhmrkkingg
provided in the supplementary material and will be
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ACKNOWLEDGMENTS
This work received funding by the German Research Foundation (project 553038473).
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A APPENDIX
A.1
BENCHMARKING TASKS
Table provides a detailed overview benchmarking tasks the related datasets.
This table provides an overview the datasets and prediction tasks included in this study, covering routine cardiac diagnostics, clinical outcomes, and patient metadata, along with their sample sizes, patient counts, and label structures. For all tasks, we report effective sample sizes rather than the total number of available ECGs. In classification tasks, the effective sample size corresponds the number of ECGs and patients with least one positive label, since samples containing only negative labels do not contribute information about the presence of condition. In regression tasks, it corresponds to the number of ECGs and patients with at least one valid numeric target value, as missing targets cannot be used for training or evaluation. Consequently, effective sample sizes may be substantially smaller than the total dataset size. We adopt this definition to provide a realistic estimate of usable data per task, ensure comparability across tasks, and avoid overstating the available training signal. t; evaluation only
Table 2:
Task
Dataset
Type
Samples
Patients Outputs
Adult ECG interpretation
ECG interpretation
PTB
Multi-label
549
290
22
ECG interpretation
Ningho
Multi-label
34,808
unknown
68
ECG interpretation
CPSC2018
Multi-label
6,867
unknown
ECG interpretation
CPSC-Fxtra
Multi-label
3,441
unknown
33
ECG interpretation
Georgia
Multi-label
10,286
unknown
50
ECG interpretation
Chapman
Multi-label
10,646
10,646
42
ECG interpretation
Chapman (rhythm)
Multi-label 10,646
10,646
9.
ECG interpretation
SPH
Multi-label 25.770
24.666
35
BCG interpretation
CODE-15%
Multi-label 345.109
233,480
6
ECG interpretation
(TBB-XI(all/sub/super)
Multi-label 21,799
18,869
71/23/5
ECG interpretation'
PTB PTB-XL [ddddig/form//ttytnn
Multi-label 21.799
18.869
44/19/12
Pediatric ECG interpretation
BCG interpretation
ZZU PECG
Multi-label 12.328
10,350
58
Cardiac structure & function 82,543 EchoNext
Echocardiogram findings
Multi-label
36,286
11
Cardiac outcomes
Cardiac discharge diagnoses
MIMIC-I-EEG
Multi-label 114,355
49,400
158
Non-cardiac outcomes
Non-cardiac discharge diagnoses
MIMIC-I-EEG
Multi-label 178,163
81,930
918
Acute care predictions
Clinical deterioration
MIMIC-IV-ECO
Multi-label
5,577
4,595
6
Mortality
MIMIC-IV-ECG
Multi-label
17.639
10,220
ICU admission
MIMIC-I--EGG
Multi-label
18,690
13,868
Patient characteristics
Sex
MIMIC-I--ECG
Binary 182,076
83,736
Age
MIMIC-IV- ECG
Regression 182,076
83,736
Biometrics
MIMIC-IV-ECC
Regression
119,214
53,702
ECG features
HIMIC-IV-ECE
Regression
181,989
83,721
Lab values
MIMIC-IV-ECG
Regression
88,448
$3,293
Vital signs
MIMIC-I--ECG
Regression
131,602
66,605
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A.2 MODEL ARCHITECTURES
A.2.1 ECG-CPC MODEL
The model architecture largely follows (Mehari Strodthoff 2023) and composed of four convolutional layers as the encoder, followed by four S4 layers (Gu ct al. 2022) (state dimension 8 and model dimension 512) as the predictor. In contrast to (Mchari & Strodthofi] 2023), the model operates at a sampling frequency of 240 Hz, which is the minimal sampling frequency in the HEEDB dataset (Koscova et al. 2024) used for pretraining. To account for the deviation from the sampling frequency in the original publication, the model uses kernel size of 3 and stride of 2 in the first convolutional layer and predicts ahead 14 steps (as compared to 12 originally) in the CPC objective.
A.2.2 S4 MODEL
The S4-based supervised baseline follows the specification in (Strodthofl et al. 2024). It also uses four S4 layers (Gu et al. 2022) (state dimension 8 and model dimension 512) without a convolutional encoder. operates at sampling frequency of 100 Hz with an input size of 2.5 seconds.
A.3 PREDICTIVE PERFORMANCE
A.3.1 FINETUNING
Quantitative finetuning results are compiled in Table 3
A.3.2
FROZEN EVALUATION
Frozen evaluation results are compiled in Table
A.3.3
LINEAR EVALUATION
Linear evaluation results are compiled Table 5
A.3.4 RANKING
In Table 6 we summarize model performance for each task in terms of ranked list with ties, accounting for statistical significance. In Table 7 the seven categories considered.
we summarize these results by reporting the median ranks for each of
A.4 LABEL-SPECIFIC MODEL PREDICTIONS
In Table Table 29 we show model predictions for the 10 best predicted labels (sorted by the performance of the supervised S4 model). These tables reflect the high degree of specificity of the tasks that are covered by this benchmark.
A.5 SCALING ANALYSIS
Table 30 lists the fit parameters for the scaling curves the form CN + Lo- Taking the supervised baseline (S4) as reference,
these parametric forms work out for given training set size N. what the required training set size would be N to reach the same level of performance with a given pretrained model. The ratio No N*/N then characterizes the improved label efficiency of the pretrained
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Table 3: Comparison of aggregated macro-AUROC for classification and MAE for regression under fine- tuning with linear prediction head. We highlight with t tasks where higher AUROC is better and tasks where lower standardized MAE values are better. The best-performing result is highlighted in boldface and underlined, while models that do not perform statistically significantly worse also highlighted in bold- face. signifies evaluation of a model trained on another dataset (listed above).
EMs (Finelumed)
Supervised
ECGFounder ECG-EEPA ST-MEM MERL ECGEM-KED HaBERT-ECG ECG-FM ECG-CPC
S4 NetDD
Adult ECG interpretation
PTB f
0.656
(U679
0.694
0.717
0.612
0.699
0.725
0.702
0A654
0.564
Ningha
0.954
0.955
0.588
CPSC1008 T
0.966
09966
0.969
0.962
0.949
CPSC-Extra f
0.906
0.597
0.883
(1873
0.824
0.766
(18622
0.89N
0.852
0.818
Georgia
0.930
0.918
0.888
0.912
0.877
0.83
0,912
0,913
0.903
0.884
Chapmnn
-Capman (chythm) ?
0
0.989
0.985
0.975
0.963
01882
0.941
0.987
0.986
0.985
SPH T
0,983
0.980
0.964
0.941
0.553
0
0
0.967
CODE-15% 1
0.963
PIB (all) 1
0:934
0.940
0,908
0.925
0.899
0915
0.927
0.949
0.941
0.929
-PTB-XI. (diag)
0,950
0.946
0.904
0.940
-PTB-XL. (form)
0.875
0.912
0.887
0.591
0.829
0.860
0.904
0.934
0.919
0.893
-PTB-XL. (daythhm)
0.500
0.956
0.937
PIB-XL (sub)
0,945
0.955
0.916
0.937
0.908
0918
0.933
0.940
0,938
0.926
PTB-XI. (super)
0.935
0.921
0.901
0.930
0.905
0.908
0,916
0.934
0,932
0.924
Pediatric BCG interpretation
ZZU PBCG
0.898
0.911
0.893
0.886
0.861
0.883
0.887
0.892
0.897
0.868
Curdiac stracture & function 0,816
EchoNest (Echo)
0.822
0.806
0.311
0.808
Cardiac outcomes
MIMIC (Candiac)
0.760
0.719
0.775
0.781
0.780
0.747
Non-cardiae
MIMIC (Non- cardiac)
0.711
0.719
0.714
10672
Acute predictions
MIMIC (Deletioration)
0.717
0.747
0.714
0.743
0.728
0.664
0,767
0.764
0.756
0,731
MIMIC (Morialing) 1
0880
0.792
0.784
0.500
0.768
0,722
0.811
0,803
0,793
0.744
MIMIC (ICU)
0.748
0.742
0.737
0.744
0.734
0.710
0.750
0.753
0.745
0.721
Patient characteristies
MIMIC' (Sex)
0504
0,885
0.916
0,100
0.933
0.919
0.869
(Age)
0.50:
0.518
MIMIC: (Biocnetrics) 1.
0.637
0.640
0.673
01625
0.647
0.725
0.620
0.604
0.626
0.684
MIMIC' (ECG Features) 4
0.458
0.460
0,500
0.463
0.466
0.529
0.408
0.451
0.452
0.486
Vilues)
0.675
0.691
MIMIC (Visal Sigus)
0.704
0.703
0.715
0.702
0.717
0.700
0
0.712
model. For each of the models, we evaluate r for different training dataset sizes. The results are compiled in Table 31
A.6 MODEL COMPLEXITY
In Table 32 we compare different notions of model complexity, namely, number of model parameters, number of floating point operations, peak GPU memory usage and inference throughput.
A.7 IMPACT OF DAPER-DEPENDENT LEARNING RATES DURING FINETUNING
In Table 34 we investigate the effect of layer-dependent learning rates during finetuning across three datasets, PTB-XL EchoNext and CPSC-Extra.
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Table 4: Comparison of aggregated macro-AUROC for classification and MAE for regression under the frozen evaluation mode. We highlight with tasks where higher AUROC is better and & tasks where lower standardized MAE values are hetter. The hest-performing result highlighted in boldface and underlined, while models that do not perform statistically significantly worse are also highlighted in boldface. signifies evaluation of model trained on another dataset (listed above).
FMs (Frusen Evaluation)
Supervised
ECGFounder ECG-EEPA ST-MEM MERL ECGEM-KED HaBERT-ECG ECG-FM ECG-CPC
S4 NetID
Adult ECG interpretation
PTB f
0.681
(1681
0.686
0.561
0.592
0.592
0.639
0.716
0A654
0.564
Ninghu
0.956
0.953
CPSC1008 T
0.966
0.975
0.956
0.956
0.919
0.930
0.989
0.962
0.949
CPSC-Extra f
0.907
0.902
0.867
01872
0.739
0.31
0.840
0.887
0.852
0.818
Georgia
0.924
0.910
0.893
0.800
0.787
0.366
0.858
0.894
0.903
0.884
Chapannn
0.954
0.917
0.943
0,963
0.953
-Caapman (chythm) ?
0,983
0.98
0.984
0.972
0.849
0.551
0,974
0.983
0.99%
0.983
SPH T
0.965
0.960
0.946
0.958
0.529
0.961
0
0.967
CODE-15% 1
0.963
0.983
PIB (all) 1
0:927
0.934
0,910
0.508
0.100
0.883
0.884
0.931
0.941
0.929
-PTB-XI. (diag)
0,940
0.918
0.940
-PTB-XI. (form)
0.876
0.589
0.894
0.879
0.783
0,815
0.838
0,905
0.919
0.893
-PTB-XL. (fhythim))
f
0.96
0917
0.951
0.95%
0.937
PIB-XL (sub)
0,9.9
0.934
0.931
0.917
0.166
0.903
0.912
0.934
0.938
0.926
PTB-XI. (super)
0.928
0,917
0.923
0.915
0.865
0,892
0.877
0.919
11932
0.924
Pediatric ECG interpretation
ZZU PBCG
0.891
0.905
0.899
0.870
0.789
0.857
0.845
0.879
0,899
0.868
Curdiac stracture & function 0,817
EchoNest (Echo) f
0.811
0.301
0
0.822
0,819
0.803
Cardiac outcomes 0.755
MIMIC (Candiac)
0.745
0.757
0.734
0.736
0.774
0.780
0.747
Non-tariiee ontcomes 0.681
MIMIC (Non- cardiac)
0.657
0.689
0.703
0.714
0.672
Acute predictions
MIMIC (Detetioration)
0.697
0.702
0.648
0.721
0.661
0.685
0.639
0.743
0.756
0.731
MIMIC (Mortaling) 1
0.769
0.788
0.754
0.761
0.720
0,723
0.731
0.785
0.793
0.744
MIMIC (ICU)
0.731
0.734
0.715
0.727
0.698
0.719
0.691
0.750
IL745
0.721
Patient characteristies
MIMIC' (Sex)
0,872
0.394
0,885
0.379
0.841
0.918
0.919
0.869
(Age)
0.484
0,501
0.511
0.544
0.466
0.518
MIMIC: (Biocnetrics) 1.
0.7012
0.700
0.681
01683
0.715
0,702
(1751)
0.640
0.626
0.684
MIMIC (BCG Features) 4
0,689
0.477
0.489
0.207
0.363
0.500
0,566
0.465
0.452
0.486
Values)
0.703
0.676
0.675
MIMIC (Visal Sigus)
0.719
0.716
0.739
0.716
0.722
0.747
0.703
0.701
0.712
A.8 BACKBONE COMPARISON FOR SUPERVISED TRAINING FROM SCRATCH
In Table 34 we provide additional results on the relative performance of different model backbone across three datasets, PTB-XL, EchoNext and CPSC-Extra. In addition to the S4-model, a structured state space model, and NetID, CNN, we consider Mamba-1 (Gu & Dao 2024) and Mamba-2 (Dao & Gu 2024) as an examples for more recent state-space model. More specifically, for the two Mamba models we use the following configuration d_model = 512, d_state 128, n_layers = 4. The S4 backbone performs best or consistent with the best-performing model across all three datasets. Net1D is competitive only on single dataset. Mamba-1 I is the best perfoming model on a single dataset. This underscores the superiority of the S4-based baseline, in line with recent claims in the literature (Somvanshi et al.; [225 Patro & Agneeswaran 2025) putting into question the suitability of latest state space models for the processing of physiological time series.
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Table 5: Comparison of aggregated macro-AUROC for classification and MAE for regression under the linear evaluation mode. We highlight with 1 tasks where higher AUROC is better and tasks where lower standardized MAE values are hetter. The hest-performing result is highlighted in boldface and underlined, while models that do not perform statistically significantly worse are also highlighted in boldface. signifies evaluation of model trained on another dataset (listed above).
FMr (Linear Evaluation)
Supervised
ECGFounder ECG-EEPA ST-MEM MERL ECGEM-KED HaBERT-ECG ECG-FM ECG-CPC
S4 NetDD
Adult ECG interpretation
PTB f
0.671
(U665
0.692
(1:833
0.563
0.604
0.692
0.578
IM654
0.564
Ningha
0.954
0.916
0.898
CPSC1008 T
0.964
0.975
0.945
0.914
0.786
0.899
0.906
0.902
0.962
0.949
CPSC-Extaa f
0.910
0.902
0.885
01858
0.553
0.555
0.842
0.794
0.852
0.818
Georgia
0,923
0.921
0.889
0.872
0.642
0.847
0.847
0.854
0.903
0.884
Chaponnn
0,968
0.916
0,904
0.868
0,963
0.953
-Caapman (chythm) ?
0.987
0.989
0.985
0.946
0.776
0.553
0.968
0.944
0.99%
0.985
SPH T
0,975
0.967
0.966
0.943
0.894
0.928
0
0.967
CODE-15% 1
0.975
0.965
0.968
0.963
PIB-XL (all) 1
0:931
0.928
0,908
0.883
0.706
0.867
0.841
0.904
0.941
0.929
-PTB-XI. (dlag))
0,947
0.925
0.903
0.8%4
0.875
0.907
0.943
0.940
-PTB-XI. (form)
0.874
0.908
0.889
0.564
0.716
0.817
0.782
0.873
0.919
0.893
-PTB-XL. (fhythhm))
0.96
0.951
0.577
0.902
0.938
0.956
0.937
PIB-XL (sub)
0.945
0.916
0.916
0.355
0.734
0.898
0.806
0.886
0.938
0.926
PTB-XI. (super)
0.924
0,911
0.896
0.887
0.802
0,877
0,873
0.863
11932
0.924
Pediatric ECG interpretation
ZZU PBCG
0,900
0
0,893
0.847
0.591
0,827
0.813
0,852
0,899
0.868
Curdiac structure & function 0.816
EchoNest
0.795
0.806
0.794
0.770
0.800
0.819
0.808
Cardiac outcomes 0.751
MIMIC (Candiac)
0:751
0.751
0.761
0.719
0.675
0.751
0.780
0.747
Non-tardiee ontcomes 0.672
MIMIC (Non- cardiac) f
0.675
0.688
0.617
0.642
0.680
0.714
0.672
Acute predictions
MIMIC (Detetioration)
0.713
0.720
0.717
0.704
0.627
0.664
0.616
0.733
0.756
0.731
MIMIC (Morialing) 1
0.774
0.782
0.788
0.744
0.681
0,722
0.676
0,769
0.793
0.744
MIMIC (ICU)
0,730
0.736
0.737
0.722
0.658
0.710
0.683
0.733
IL745
0.721
Patient characteristies
MIMIC' (Sex)
0,872
0,883
0,882
0.353
0,110
0,853
0.873
0.919
0.869
(Age)
0,489
0,503
0.579
0.551
0,455
0.518
MIMIC: (Biocnetrics) 1.
0.700
0.685
0.674
0.711
0.740
0.725
01857
0.686
0.626
0.684
MIMIC (BCG Features) 4
0.488
0.490
0.499
0.543
0.606
0.529
0.617
0.304
0.452
0.486
Values)
0.695
0.608
0.709
0.688
1.675
MIMIC (Visal Sigus)
0.716
0.714
0.711
0.719
0.717
0.834
0.708
0.701
0.712
A.9 REPRESENTATIONAL ANALYSIS BASED ON CKA
A.9.1 INTRA-MODEL ANALYSIS
We start by describing the intra-model CKA patterns in detail. We investigate the FMs in their original form without any task specific finetuning. The results are compiled in Figure
ECG-CPC In Figure 4a the early CNN layers (CNNO-CNN2) show a very high similarity suggesting that they learn very similar representations, which could be taken as sign that the CNN encoder is potentially overparameterized for the task. The final CNN layer (CNN3) shows similarity with both the earlier CNN layer as well as the first SSM layer (S4-0) thereby serving as a transition point between the local, CNN-based feature extractor and the sequential SSM layers in the hybrid architecture. The still pronounced dissimilarity between the the final CNN layer (CNN3) and the first SSM layer (S4-0) can most likely be explained by the difference in model architecture between these two layers. The following S4 layers become increasingly dissimilar to the CNN encoder layers, suggesting a substantial evolution throughout the architecture. Finally, the last S4 layer (S4-3) is highly specialized and shows only moderate to low similarity to other S4 layers,
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Table 6: Statistical ranking FMs across evaluation modes and datasets. Rankings (Fine- tumed/Frozee/LLimea)) assigned based statistical equivalence groups determined by bootstrap testing, where models not performing significantly worse than the best model share the same rank. Lower ranks indicate better performance. signifies evaluation of model trained on another dataset (listed above).
Finetunod/
Supervised
ECGFuunder
HoBERT-BCG ECG-FM ECG-CPC
NedID
Adult ECG interpretation
1/1/6
10996
Nangbo
1/41
1/1/1
714:5
77715
10/10/10
77525
1/66
1868
1/1/1
6/11
CPSC2018
4/22
1/111
6364
9/226
9/10110
63378
1/86
1/218
4/2/
6/8/4
CPSC-Fxtra
1/A
5165
563
Georgia
Chipmon
11/1
1/111
6/414
6/4/5
10/10/10
91966
38/
3/78
3/11
6444
Chapman (shyllmn)
SPH
1/32
10/10110
S/8/7
1/1/1
S/V2
CODE- 15%
S/SA1
1/11
315/1
5/9/9
10/10/10
1/516
S/516
1/1/6
1/1/1
5/14
PTB-XL (all)
2/2/
2/2/2
81615
S16/7
10V10110
S2878
5/8/9
1/2/5
2/1/
5/2/2
-PTB-XI. (ding)
1/1/
1/1/4
9665
4167
9/10/10
77828
7/6/8
1/51
4/1/1
4/1/3
-PTB-XL (form)
-TBB XL (riyythm)?
1/1
1//1
5143
9/7/7
10/10/10
5/7/7
1/09
1M15
S/1/3
8/7/5
PTB-XL isub)
10/
10710/10
1/099
1/1/1
(super)
5/10110
5/4/
Podiatrie RCG interpretation
ZZU PBCG
2/41
1//1
2/1/1
2/6/7
9/10/10
716/7
2AV5
2/111
9/6/5
structure
EchoNext (Echo)
2/16
2/43
2/1/1
2/775
7/8/8
9/9/
1098
1/13
2/1/1
7/4/3
Cardiac outeumes 3/13
MIMIC (Cardiac)
6/5/3
3/V3
8/7/2
65999
10/388
3/10/
17273
1/1/1
9/5/3
Non-cardiac vutcomes
MIMIC (Non- cardica
6/577
4/3/4
8/122
4/815
6/999
107288
1/10/10
172/3
3/1/1
94504
Acute care predictions
MIMIC (Deteriontion)
6/425
194/1
67515
1:4/5
6/8/5
1048
1868
1/1/1
1/11
6/11
(Mortality)
(ICU)
9/5/5
Patient characteristics
MIMIC (Sex)
S/644
6/V2
3/4/2
214/7
67839
1078
2/10/7
1/1/4
2/1/1
9/044
MIMIC (Aga)
3/544
5032
8/4/3
3/577
5/10/10
10/5/5
1/U78
2/2/6
4/1/1
9/7/5
MIMIC (Biometrics)
S16/6
S15/
8/3/2
2/3/7
7/919
101675
2/10/10
1/2/3
2/1/1
9/3/
(BCG Features)
2//1
1/422
MIMIC (Lab Values)
57641
1/36
6992
1/515
678/9
10/68
6/10/10
1/1/2
1/11
9/3/4
MIMIC (Vinal Sigos)
6/425
344/5
9/33
314/8
6/859
9/777
3/10/10
1/2/2
1/11
8/333
Table 7: Median statistical rankings of FMs across evaluation modes by categories. Rankings (Fine- fune/FFrozen/lineee represent the median performance position across all datasets within each category. Lower values indicate better overall performance.
FinetuneddFrozee/LLieenr
Supervised
ECGFounder RCG-JEPA
HoBERTEEGG ECG-FM ECG-CPC
Adult BCG interpretation
1/11
1/1/1
6/344
S/6/6
10/10/10
7//7
3/8/7
1/3/7
3/1/1
6/4/4
ECG interpretation
Cardiac structure fuaction
2/4/6
2/433
2/1/1
2/7/6
7//88
9//8
10/5/8
1/1/3
2/1/1
7/4/3
Cardiac outcomes
Non-cardiac ouscomes
6/5/7
4/3/4
8/1/2
4/4/4
6/99
10/1/8
1/10/10
1/2/3
3/1/1
9/5/4
Acute case predictions
Patical claracteristies
5/5.5/4
4.5/3/3.5
8/4,5/2.5
2,5/4.3/7
6/8.599
10/7/8
2
1/2/3.5
2/1/1
9/3.5/3.5
suggesting it learns the most task-specific representations for the CPC task. Overall the model shows a clear to high-level sequential features.
representational gradient from low-level
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Table 8: Finetuning with a linear prediction head performance for the 10 best best- predicted labels, sorted by supervised S4 AUROC the PTB dataset.
Label
ECGFounder
ECG-JEPA
ECGEM-KED
ST.MEM
HURERT-ECG
ECGJEM
ECG-CPC
S4
Healthy control
0.968
0.919
0.901
0.917
0.831
0.922
0.911
0.884
0.581
0.920
Atterial [lypemension
0.638
0.400
0.721
0.705
0.310
0,610
0,924
0.619
0.876
0.352
Atiil fibrillation
0.997
0.430
0.955
0.780
0.372
Conlionyopully
0.812
0.13
0.731
0.923
0.909
0.740
0.875
0.764
0.822
Ventricular úbcillation
0.665
0
0.528
0.786
0.547
0,709
0.581
0.561
0.736
0.661
Myscardial in inforction acule
0.686
0.330
0,706
0,819
0.808
0:414
0.630
0.793
0.722
0.731
Myocardial inforction old
0.790
0.675
0.758
0.717
0.696
Myocardial inforction acule catheterized
0.778
0.760
0.828
0.736
0.652
0.767
0.824
0.710
0.715
0.696
Obesity
0.498
0.755
0.682
0.557
0.668
0,430
0.647
0.796
0.700
0.507
Atterall hypersension
0.552
0,713
0.621
0,743
0.654
0.704
0.674
0,680
0.693
0.556
Table 9: Finetuning with linear prediction head performance for the 10 best-predicted labels, sorted by supervised S4 AUROC on the Ningbo dataset.
Label
DCGFounder
ECG-JEPA
ECGEM-KED
MERL
ST-MEM
HoBERT-ECG
ECG-EM
BCG-CPC
S4
Net1D
Woolf-Parkknnso--W White
1,000
1.000
1,000
1.0001
(1597
0.99%
1,000
1,000
1,000
1,000
AAR
0.597
0.559
0.398
0.559)
0.997
0.559
0.998
Nentrkular fibiillation
1,000
1.000)
1.000
0.994
0.930
1,000
1.000
0.999
0.999
0.999
Atrial fltter
0.999
0.999
0.998
0.998
0.59%
0.997
0.999
0.998
0.999
0.99%
Right amal hypertrophy
0,596
0.959
1.000
1,000
0.905
0.987
0.998
0.988
Ventrcular eacape fhythm
0,999
0.999
0
0.999
0.998
0.999
Samus tccycandia
0
0.997
0.995
0.996
0.595
0.996
0.9977
0.998
0.998
0.947
lunetional tachycardia
0.985
1.000
0.996
0.972
0.597
1,000
0.999
0.992
0.998
0.997
Sinus bradycaddia
0.998
0.998
Complese left branch black
0.958
0.998
0.999
0,998
0.597
0.958
Table 10: Finetuning with a linear prediction head performance for the 10 best-predicted labels, sorted by supervised S4 AUROC on the CPSC2018 8 dataset.
Label
ECGFounder
ECG-JEPA
ECGEM-KED
MERL
ST-MEM
HoBERT-ECG
ECG-FM
ECG-CPC
Net1D
Left bundie brancha blocs
0,998
0,599
0,94%
0.999
0.948
0.599
0,999
0.998
0.9-3
0,998
1st degree actoventricular block
0,990
0.990
0,971
0.963
0.966
0
0.980
0.982
0.957
0,982
Attal fibillation
0,994
0.580
0,983
0.982
0,978
0.991
0.995
0.888
Right bundle
0,981
0.971
0.982
0.974
0.983
0,980
NORMAL
0,965
0,973
0,951
0,948
0,930
0.917
0.963
0.973
0,951
0,961
SI depression
0.963
0.974
0,945
01943
0.943
0.932
0.963
0.973
0.961
0.957
Premature veatricular contraction
0
0.957
0.945
0,891
0.874
0.914
0.969
0.916
0,948
0.931
0.925
0.962
0.938
Premature atrial contraction
0.927
0.50
0,884
0,828
0,835
0.941
0.957
0.928
0,890
0,881
Table 11: Finetuning with a linear prediction head performance for the 10 best-predicted labels, sorted by supervised S4 AUROC on the CPSC-Extra dataset.
Label
ECGFoudder
ECGJEPA
ECGEM--KED
ST-MEM
HOBERT-EGG
ECG-CPU
S4
NetID
Right atrial hypertrophy
1,0000
03352)
0.993
0.95%
0.979
(1.5%
0,975
01979
1,000
0.12
Complete bearl block
0.999
1,000
0.999
0.999
0.967
0.997
1,000
1,000
0.996
0.999
3nd degres atriowntricalar block
1.000
0.9%
0.994
Complete right handle branch
0.994
0.989
0.995
0.94
0.988
0.73
Sinus rachycordia
0.986
0.988
0,990
0.978
0.922
0.989
0.987
0.990
0.985
0.987
Atrial fivillatiin and flutter
0.990
0. 0.98
0.984
0,930
0.913
0.999
0,983
0.99
0.979
0.97
Bendyeardia
0.978
0.80
Atrial fltlir
0.985
0.990
0.990
0.990
0.919
0.948
0.902
0.975
0.977
0.962
Incomplete right hundle branch block
0.982
0.976
0.978
0.977
0.941
(1971
0.965
(U986
0.975
0.940
Atrial achyeundia
0,990
0.959
0.991
0,976
0,926
0.997
0.991
0.978
0.972
0.516
ECGFounder In Figure 4b), we show the CKA analysis for the ECGFounder model. The convolutional stem shows low similarity with all other layers. Apart from that, the CNN model shows a gradual transition across layers with no abrupt transitions as observed in the hybrid ECG-CPC model between the convolutional fea- ture extractor and the S4 layers. The model shows highly similar representations between SO and S4, which suggests that these layers learn quite similar representation, suggesting a potential overparameterization in the middle of the network. The final two layers (S5, S6) show less similarity with earlier layer, suggesting that these are used for task specialization.
23

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Published as a conference paper at ICLR 2026
Table 12: Finetuning with a linear prediction head performance for the 10 best-predicted labels, sorted by supervised S4 AUROC on the Georgia dataset.
Label
ECGFounder
JEG.JEPA
ECGEM-KED
MERL
ST-MEMM
HOBERT-ECG
ECG.FM
ECG-CPC
S4
2nd degres anrovvntricsaar block
0.999
0.997
0.999
0.998
0.998
0.997
0.998
1.000
1.000
1.000
Left bundle branch block
0.998
0,998
0,998
0.997
0.981
0.998
0.997
0.998
0.997
0.998
bradycandia
0.997
0.989
0.979
Simss tachycandia
0.995
0.988
0.988
0.981
0.973
0.994
0.984
0.994
0.993
0.991
Sugeaventriculur cachycardia
0.995
0,990
0.991
1,000
0.985
0.996
0.978
0,995
0.988
0.958
Ventricular pacing pallern
0.922
0
0.967
0.972
0.982
0.968
0.908
0.979
0.982
0.725
anterior
black
0.986
0.956
0.981
0.982
Right bundle branch block
0.992
0.980
0.987
0.982
0.982
0.986
0.986
0.996
0.980
0.982
Bundle brunch block
0.978
0
0.941
0,960
0.951
0.962
0.928
0.969
0.976
0.97
degree arroventicuuur block
0.984
0,960
0,984
0.975
0.946
0.943
0.983
0.983
0,976
0.910
Table 13: Finetuning with a linear prediction head performance for the 10 best-predicted labels, sorted by supervised S4 AUROC on the Chapman dataset.
Label
ECGFounder
ECG-JKPA
ROGEM-KED
ME.RL
ST-EEM
HoBEKT-ECC
ECG-EM
ECG-CPC
84
NetDD
Myscardial infarction
0.999
0.597
1,000
1,0000
0.999
1,000
Sinus bradycardia
1,000
1,000
0.999
0.986
0.991
0.999
0.999
0.999
0.999
0.999
Sinus tcchcardia
0.999
0.598
0.994
0.955
0.991
0.996
0,999
0.999
0.998
0.997
Myocardial infaretion in the front wall
0.997
0,995
0.994
0.998
1.999
0.993
0.997
0.997
0.997
0.987
Supraventricule tachycandia
0.998
Long RR interval
0.997
0.999
0.999
0.995
0.954
0.996
0.994
0.996
0.906
0.995
Anial lihrillation
0.9997
(1556
0.95%
0.994
0.967
0.992
0,998
0.996
0.994
0.992
Attial flutier
0,990
0.984
0.987
0.944
0.909
0.978
0.996
0.992
0.991
0,992
bundle beanch block
0.914
0.980
0.974
Right bundle branch block
0.996
0.995
0.980
0.993
0.959
0.993
0.991
0.985
0.991
0.991
Table 14: Finetuning with a linear prediction head performance for the 10 best-predicted labels, sorted by supervised S4 AUROC on the SPH dataset.
ECGlounder
ECG-EFA
ECGEM-KED
ST-MEM
HoBERRT-CGG
ECG-EM
ECG-CPL
AV boock, complete (mard-dogree)
L000
1,000
1.000
1.000
0.934
1,000
1.000
1,000
1,0000
1,000
Left bundle- mendee--mmnch block
L000
1.000
0.999
1.000
1,000
1.000
1.000
1.000
1,000
0.999
Tunctional escape complex(es)
1000
1,000
1,0000
1.000
1.000
1,000
Right bundle-branch black
1,000
0.999
0.559
0.999
0.599
1,000
0.999
2:1 AV block
1,000
0.997
1,000
0.991
0.990
0.909
0.999
0.998
0.999
0.994
Atrisl fibillation
1.000
1,000
1,000
0.999
0.937
0.900
1,000
0.999
0.999
0.998
Prolonged interval
0.990
0,989
0.977
0.996
0,997
Anterior
0.905
0.996
0.991
0.986
1.908
0.999
0.994
0.995
Simns bendycandia
0.995
0,905
0.994
0.980
0.991
0.994
0.995
0.995
0.995
0.994
Early repolatication
0
0,983
0.967
0,997
0,964
0.929
0.988
0.982
0,995
0.982
Table 15: Finetuning with linear prediction head performance for the 6 labels, sorted by supervised S4 AUROC on the C0DE-15% dataset.
Label
ECGFounder
EOG-JEPA
ECGPM-KKED MERL
ST-MEM HOBERT-ECG
EUG-PMI
ECG-CPC
S4
NeilD
Left bundle branch block
0,997
11.998
0.967
0,394
03556
0.914
03597
0.99%
0.995
Sinus tachycurdia
0.994
0.994
0.994
0.902
0.958
0.993
0.991
0.994
0.995
0.995
Right bundle branch block
0.969
D.986
0.955
0.991
0.951
0.991
0.979
0184
0.990
0.977
Atrial firillation
0.992
0
0.988
0.992
0.958
01591
0.981
0.992
0.989
0.982
1st degene atroventricular bleck
0.950
0.987
0.984
0.964
0.976
Simns hrndycardia
0.985
0.929
0.958
0.955
0.550
0.990
0.980
0.984
0.969
ECG-JEPA Figure shows the CKA analysis for the ECG-JEPA transformer model. Again the embedding layer shows little similarity to all other layers. The first transformer layer (BIkO) serves a transition point between the embedding layer and the following transformer layers, showing moderate similarity to both. As striking feature, the following transformer layers (BIk1-BIk BBk shows very high similarity, which suggests that the representational structure barely changes across the 10 layers. This might be indicative for a representation collapse and possible inefficiency in the architecture where most layer contribute only min- imally distinct representations. The representations of the final transformer layer (Blk11) 1) deviate strongly from the previous, suggesting that the former is used for task specialization.
24

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Published as a conference paper at ICLR 2026
Table 16: Finetuning with a linear prediction head performance for the 10 best-predicted labels, sorted by
supervised S4 AUROC on the PTB-XL (all) dataset.
Label
ECGFoudder
ECG.JEPA
ECGFALKED
MERL
ST-MEM
HoBERT-ECG
ECGJEM
ECG-CPC
54
NetID
Suhendoconfial injury in inferior leads
0.997
0 1.908
1.000
0.559
0.994
0.903
0.992
0.998
1.000
0.996
Complete right bundle branch block
0,999
0.998
0.998
0.598
0.997
0.997
0.995
0.996
0.9%
0.997
Cumplete left hundle branch black
0.998
0.903
0.998
Parosyomal uppreentriculr tichycrrdia
1,000
0.988
0.997
0.988
0.993
0.998
1,000
0.998
0.9%8
0.995
Sinus tachycrriia
0.996
0.991
0,991
0.991
0.957
0.986
0,990
0.996
0.995
0.991
Septal hyperinophy
0.976
0.999
0.972
0.994
0.952
0.996
0,976
0.999
0.994
1,000
Posteierr
infarction
0.996
0.991
0.994
Third degree AV block
0.999
0.996
0.989
0.992
0,989
0.962
0.998
0.992
0.990
0.973
Suhendacantial injury in interovepial leads
0.992
0.994
0.964
0.991
0.963
0.989
0.983
0.993
0.950
0.987
Ventiiullar complex
0.988
0.991
0.954
0,875
0.992
0,984
0,986
0.983
0,975
Table 17: Finetuning with a linear prediction head performance for the 10 best-predicted labels, sorted by supervised S4 AUROC on the ZZU pECG dataset.
Label
ECGFounder
ECG-JEPA
ECGEN-RED
MERL
ST-MIEM
HUBERFFECG
ECG-IM
BCG-CPC
84
NetID
Ventricular escape complexies)
1
0.589
1.000
0.945
0.359
0.999
1,000
1.000
0.959
0.990
Nentricular tnchccnniin
0:997
0,999
0.999
0.999
0.999
0.994
0.998
0.999
0.999
0.998
Sinus pause arrest
0.963
0.968
0.956
0.989
0.892
0.988
0.872
0.975
0.998
0.992
block, advaneed (high-gnde)
0.998
0.967
0.906
0.965
0.985
0.963
Socond-dogree AV block, Mobitz type KWanckehach)
0:959
0.999
0.999
0.71
0.958
1,060
0.994
0.988
0.974
Right bundle branch block
0.995
0.997
0.995
0.982
0.968
0.988
0.992
0.995
0.989
0.990
AV block, complete (thind-lagree)
0.996
0,999
0.999
0 0.99
0.987
0.996
0.998
0.997
0.987
0.998
Anril flatter
0.804
0.979
TU fusion
0.981
0.957
0.960
0.997
0.958
0.912
0.947
0.989
0.964
0.92]
Sinus bralycardia
0.984
0.974
0.981
0.982
0.979
0.984
0.976
0,986
0.984
0.985
Table 18: Finetuning with a linear prediction head performance for the 10 best-predicted labels, sorted by supervised S4 AUROC on the EchoNext dataset.
Label
ECGFoudder
ECG-JRPA
RCGEM-KED
MERI
ST-MRM
HURKRT-ECG ECG-FM
ECG-CPC
NedDD
Tvee lie flgg
0.301
0.897
0.356
0.902
0.333
0.878
0.873
0.910
0.397
0.879
re systelic dycfunction moderste or grenter fleg
0.879
0.891
0.581
0.883
0.577
0.861
0.856
0.899
0.800
0.880
aortic ssenusis moderate greater flag tricuspid regurpittiion moderatee preater flug
0.821
0.838
0.830
0.543
0.314
0.790
0.767
0.8:9
0.557
0.808
0.845
0.840
0.845
0.525
mitral regurgiatioon moderate or greater flag
0.836
0.817
0.828
0.355
0.813
0,805
0.789
0.833
0.829
0,807
pulmonar regurgiaation moderate greater fag
0.875
0.857
0,880
0.872
0.853
0.825
0.861
0.670
0.828
0.85)
pasp 45 flog
0.783
0.794
0.774
0.789
0.773
0.759
0.725
0,810
0.793
0.766
ir max gio flag
0.779
0.757
0.781
0.764
0.722
nortic regurgivation muderate or greater fag lowi gie 13 flig
0.751
0.734
0.751
0.760
0.747
0.755
0.724
0.758
0.776
0.752
0.767
0.768
0.762
0.774
0.754
0,753
0.706
0,779
0.774
0.755
Table 19: Finetuning with a linear prediction head performance for the 10 best-predicted labels, sorted by supervised S4 AUROC on the MIMIC (cardiac) dataset.
Label
ECGiounder
ECG-JEPA EUGEM-KED
MERL
ST-MEM
HoBERT-ECG ECG-EM
ECG-CPC S4
NetiD
T:47 Left bluck
0.935
0.923
0.932
0.921
(191)
14510 Right bundle black
0.936
0.925
0.931
0.937
0.930
0.912
0.915
0.919
0.926
19023 Acutzchroeti systolic HF
0.900
0.900
0.899
0.900
0.894
0.891
01892
0.907
0.906
0.804
1428 Other condiony opathies
0.881
0.892
0.884
0.885
0.883
0.86]
0.582
0.891
0
0.878
1255 Ischamic contiengepulty
0.902
0.882
0.886
0.570
1132 HTN hean+CKD will;, ESRD
0.897
0.899
0.873
0.893
0.913
0.847
0.886
0.912
0.396
0.876
1-82 Chromic AF
0.895
0.894
0.892
0.885
0.862
0.865
0.582
0.899
0.893
0.872
1211 STEMI, inferior wall
0.896
0.881
0.867
0.876
0.838
0.776
0.794
0.885
0.887
0.754
R178 Rheumatic tricspid discase
0.867
0.879
0.542
144 AV + LBBB
0.876
0.869
0.876
0,866
0.868
0.855
0.869
0.879
0.875
0.850
we see a similar overall structure as for the also transformer-based ECG-JEPA model. The embedding layer and the first transformer layer are distinct from largely homogeneous block of following layers (BIkI-Ilk)), the final layers (BIk10-BIkII) specialize. Compared to ECG-JEPA. ST- MEM shows slightly better gradient through the middle layers, still the middle part of the architecture remains quite homogeneous, which again suggests wasted capacity. A remarkable difference compared ECG-JEPA is the low similarity of the first transformer block (BIkO) with the following blocks (Blk1
ST-MEM In Figure 4d
25

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Published as a conference paper ICLR 2026
Table 20: Finetuning with a linear prediction head performance for the 10 best predicted labels, sorted by supervised S4 AUROC the MIMIC (non-cardiac) dataset.
Label
ECGFounder
ECG.JEPA
ECGEM-KED
ST-EEM
HUBERT-ECC
ECG-CPC
54
1,9740 Chronic oleer hed/midfoot
0.841
0.854
0.762
0.908
0.897
0.764
0.886
0.904
0.941
0.505
2/502 ICD defibrillatee management
0.945
0.918
0.536
0.929
0.910
0.950
0.919
0.935
0.936
0.919
Hepattonnnl syndome
0.631
0.903
BM 19.9 or less, whalt
0,885
0.896
0.881
0.812
0.918
0.901
0.834
V850 Driver, constraction vehicle accident
0.850
0.871
0.850
0.89]
0.912
0.711
0.877
0,913
0.893
0.810
7992 Remal dialysis dependence
0.878
0.886
0.876
0.888
0.867
0.823
0.586
0.891
0.886
0.850
oocupant collision
E660 Obesity, excess calories
0.860
0.855
0.810
0.868
0.874
0.757
0.386
0.897
0.882
0.301
N186 End-stage remal diseaoe
0.875
0.882
0.866
0.884
0.867
0.819
0.883
0.890
0.882
0.549
V-22 Outside
collasion
0.368
0.897
0.855
0.809
0.882
0.369
Table 21: Finetuning with a linear prediction head performance for the clinical deterioration labels, sorted by supervised S4 AUROC on the MIMIC (deterioration) dataset.
Lahel
ECGFonnder
ECG-JEPA ECGFM-KEDD MERL
ST-MEM
HoBERT-ECG
ECG-FM
ECG-CPC
54
NetID
arrest
0.888
018522
0.352
vasopresoees
0.771
0.768
0.764
0.750
0.739
0.719
0.771
0.800
0.800
0.774
como
0,724
0.798
0,259
0.748
0,712
(1586
0.841
0.519
0.788
0.783
mechanical venilaation
0.786
0.781
0.265
0:785
0.785
inotrupes
severe hypoxemia
0.498
0.553
0.458
0.573
0.591
0.470
0.591
0.555
0.530
0.559
Table 22: Finetuning with a linear prediction head performance for the mortality labels, sorted by supervised S4 AUROC on the MIMIC (mortality) dataset.
Label mosallt ld
BCGF ounder
EUG-JEPA
ECGEMI-KED
MERL
ST-MEM
HaBEKTTECG
ECG-PM
ECG-CPC
54
NetID
0.829
01.33
0.895
015443
0.8017
0.825
0.840
0.876
0.835
0.794
mostality Rd
0.816
mosality
0.811
0.508
0.791
0.730
0.807
0.780
monaltt 365d
0.798
0.708
0.776
0.800
0.791
0.731
0.805
0.509
0.802
0.770)
mortality 90d
0,791
0.795
0,770
0.791
0.779
0.719
0,800
0.503
0.796
0.765
mosality 180d
0.792
0.763
mostalit styy
0.836
0.671
0.755
0.625
0.552
0.787
0.679
0.705
0.548
Table 23: Finetuning with a linear prediction head performance for the ICU labels, sorted by supervised S4 AUROC the MIMIC (ieu) dataset.
ECGFounder
EOGJEEA
ECGEALKED
ST-MEMT
ECG.EM
ECG.CPC
Jou stay
0.751
0.447
0.741
0.749
0.735
0.713
0.755
0.37
0.748
0.726
24h
0,744
0.738
0.734
0.739
0,729
0,706
0,745
0,749
0,742
0.716
Table 24: Finetuning with linear prediction head performance for the sex label, sorted by supervised S4 AUROC on the MIMIC (sex) dataset.
Label ECGFounder ECG-JEPA ECGEM-KED MERL STAIE HOBERT-ECG ECG-FM BCG-CPC S4 NedD
sex
0.NIO
0,933
Table 25: Finetuning with a linear prediction head performance for the age label based on MAE on the MIMIC (age) dataset.
Label Units ECGFounder
ECG-EEA ECGEM-KED
MERL ST-MEM HOBERT-ECG
ECG-FM ECG-CPC
NeelD
Age
Years
8.78
8.82
8.88
8.54
9.59
11.03
7,835
8.32
8.66
9.86
15.07
onwards). This suggests representational bottleneck early in the network which prevents the propagation of information from the embeddings into the following transformer blocks.
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Table 26: Finetuning with a linear prediction head performance for the biometrics labels, sorted by super- vised S4 MAE the MIMIC (biometrics) dataset.
ECGFounder
ECG-JEPA
EOGEMLKED
ST-MEM
HUBERT-ECC
ECG.FA
ECG.CPC
Baselline
Feight
Inches
2,59
2.70
2.71
2.66
2.78
3.02
2,804
2.58
266
2.86
3.30
Weight
lbs
27.85
28.27
26.34
2733
29.79
32.01
26,417
26.38
27.53
30,16
35.73
BMI
kpmm
4.14
4.12
4.23
4.0
4,40
4.67
3.43
5.30
Table 27: Finetuning with linear prediction head performance for the ECG features labels, sorted by supervised S4 MAE on the MIMIC (ECG features) dataset
Label
Unils
ECGoounder
ECG-JEPA
ECGEM-KED
MERL
ST-MEM
HOBERT-ECG
ECG-FM
BUG-CFC
NetDD
Fsaseliee
mi
107.01
106.59
108.28
108.98
108.93
112.913
105.73
154.38
QRS
196
7.32
7.59
7.65
1.27
7.50
8.79
7.150
7.18
7.13
8:22
15.48
1908
26.33
26,51
26,85
26.63
26.86
28.55
27.153
26.14
26.16
27.36
57.84
18.09
16.55
16.21
16,300
15.97
QRS axis
15.07
15.41
16.01
15.86
20.39
18.22
15.849
15.35
15.35
16.41
36.06
T wave axis
24.36
24.19
24.75
34.59
28.31
30.95
24.648
24.05
23,94
25.34
35.41
Table 28: Finetuning with linear prediction head performance for the laboratory values predictions, sorted by supervised S4 MAE on the MIMIC (laboratory values) dataset
Label
Liifs
ECGFondder
EUG-EEPA
ECGEM-KED
MERL
ST-MEM
HUBEKT-ECG
ECG-EM
EUG-CPC
S4
NetiD
Baseline
Albumin
0.44
0.43
0.44
0.44
Anion Gap
mEg/L.
Bicurbnnte
mEg/.
2,66
2.65
2.67
2.68
2.69
2.72
2,993
2.64
2.67
2.68
2.71
Biliniin, Total
mgiil.
0.55
0.56
0.56
0.55
(L55
0.56
0.551
0.56
0.55
1LS5
0.67
Calcium, Total
0.51
Creatinine
migil.
0.30
0.40
0.39
0:40
0.39
0.38
0.48
Ferrión
ag/ml.
325.01
214.00
250.76
232.05
548, 12
$82.04
826,000
308.41
206. 29
280
207.79
Urea Nütrogen
mpkiL.
8.15
8.11
8.25
8.08
8.30
8.45
BA15
7,98
8.05
K.33
9,94
Henatocrit
Howoglobin
1,48
1,38
1,59
Lymphocyytes
8.55
8.57
8.63
8.57
8,70
8,89
K,400
3.48
8.56
8.78
9.37
MCHC
1.10
1.88
1.09
1.08
1,10
1,10
1.82
LJI7
1.07
1,08
1.13
Red Bloud Cells
m/pal.
0.47
0.46
0.47
0.46
0:46
0.46
0.48
0.52
RDW-SD
4.48
4.47
4.52
4.50
4.58
4.71
4.465
4.55
4.49
4.61
5.31
Creatine Kinase
TUVL
264.66
232.67
229
237.43
272.06
219.10
238.532
243.92
242.04
225.86
275.63
NTpm/BNP
potml
4538.09
Table 29; Finetuning with linear prediction head performance for the vital signs predictions, sorted by supervised MAE on the MIMIC (vital signs) dataset
Label
Units
E.G.oueder
ECG-JEPA
ECGEM-KED
MERL
ST-MEM
HUBERT-ECG
ECG-EM ECG-LPC
S4
NetlD
Baseline
11.41
11.38
11.43
11.41
11.48
11.353
11.36
11.41
11.5%
11.83
heartrate
11.61
11.67
11.68
11.68
11.788
Og sat
1.54
1.54
1.55
1.53
1:55
1.57
1.535
1.53
1.54
1.56
1.64
resprate
bpm
2.20
2.18
2.19
2.18
2:25
2.20
2:232
2.18
2.18
2.19
2.29
17.36
17,102
Kemperature
0.62
0.62
0.615
A.9.2 IODER-MODEL ANALYSIS
In Figure 5 we analyze the representational similarity across different models three different points in the model architectures (early: after the encoder/stem//ookenizrr mid: after the first half of the processing layers; late: after the final processing layer).
the early representations, shown in Figure 5a the two transformers show high mutual similarity while the con-transformer models show other patterns. This might be taken hint it is the architecture which shapes early representations and not the training objective. ECGFounder's early features are most dissimilar all other features, indicating that the pure CNN learns qualitatively different early features.
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Table 30: Fit parameters for the scaling analysis
Model
C
Lo
R²
ECGFounder (pretrained)
0,462
0.109
0.018
0.933
ECGFounder (from scratch)
0.887
0.270
0.120
0.998
ECG-JEPA (pretrained)
0.402
0.083
1.32
10-13
0.989
ECG-CPC (pretrained)
0,463
0.104
4.35 X 10-7
0.946
ECG-CPC (scratch)
0.501
0.101
9.13
10 10-10
0.957
S4
0.677
0.206
0.089
0.983
Table 31: Label efficiency for different training datasets
Model
r(N 250)
r(N = 500)
r(N 1000)
r(N 2000)
ECGFounder
0.30
0.40
0.51
0.62
ECG-JEPA
0.11
0.17
0.27
0.40
ECG-CPC
0.21
0.27
0.34
0.40
Table 32: Unified comparison of computational cost, memory usage, and inference efficiency for all ECG representation learning models. Reported metrics include: (i) GFLOPs for forward (F) and backward (B) passes (lower is better), measured with batch size 1 inference (lower is better), measured on PTB-XL (all) with batch size 64; and (iii) throughput (samples/s; higher is better) and latency (ms/sample; lower is better) under the same hardware and batch size. Timesteps correspond to the input sequence length for each model, and parameter counts include all trainable weights. The three best, i.e., most efficient, FMs are marked in bold face. The overall best model is additionally underlined.
NVIDIA L40; (ii) peak GPU memory during
Model
Timesteps
Parameters
GFLOP (I/B)
Mem
Thr Lat.
BCGFounder (CNN)
33.8M
0.602/5.066
187.37
2220.67 0.450
BCG-JEPA (Transformer)
2500
87.2M
73.877
2136.79
98.71 10.13]
ST-MEM (Transformer)
600
90.3M
20.926 62.779
609.71
368.64/2.77
MERL (CNN)
2.582
ECGEM KED (CNN)
5000
9.7M
5.423 16.258
427.39
1631,28 0.613
(Transformer)
18.829
845.27
213.87
BCG-FM (Transformer)
2500
93.9M
29,242/ 109.794
1091.71
692.35 1.444
ECG-CPC (SSM)
600
3.8M
1.741 5.213
482.32
442.3% 2.260
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Table 33: Impact of layer-dependent learning rates model performance. AUROC scores comparing models trained with and without layer-dependent learning rate scheduling. Layer-dependent learning rates apply different learning rates across network layers, typically with lower rates for earlier layers. Higher AUROC values indicate better performance. Best scores are marked in bold face. Underlined values indicate models pretrained on the corresponding dataset.
Model
PTB-XL (all) With f Without f
EchoNext
CPSC-Extra
With Without f
With / Without
ECGFounder (CNN)
0.934 / 0.936
0.817 0.818
0.906 / 0.907
ECG-JEPA (Transformer)
0.940 / 0.779
0.817 0.730
0.897 / 0.790
ST-MEM (Transformer)
0.908 0.891
0.816 0.751
0.883 0.853
MERL (CNN)
0.925 0,919
0,822 0,823
0.873 0,852
ECGFM-KED (CNN)
0.889 0,918
0.806 0,817
0.824 0,833
HuBERT-ECG (Transformer)
0.915 / 0.499
0.792 0.500
0.876 / 0.513
ECG-FM (Transformer)
0.927 0,504
0.772 0,499
0.862 0,517
ECG-CPC (SSM)
0.949 0,938
0.831 0.828
0.898 0.875
Table 34: Comparison of aggregated macro-AUROC scores for different model architectures trained from scratch. Higher AUROC values indicate better performance. The best-performing result is highlighted in boldface and underlined, while models that do not perform statistically significantly worse are also high- lighted in boldface.
Model
PTB-XL (all)
EchoNext
CPSC-Extra 1
S4 (SSM)
0.941
0.819
0.852
Detl (CNN)
0.929
0.818
0.803
Mamba-1 (SSM)
0.895
0.770
0.858
Mamba-2 (SSM)
0.914
0.807
0.844
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DAL Beer
16.99
(a) Early stage
(b) Mid stage
(c) Late stage
Figure 5: Inter-model representational similarity across network depths. CKA heatmaps comparing corre- sponding stages across four FMs (ECGFounder, ECG-JEPA, ST-MEM, ECG-CPC), Higher values (yellow) indicate similar representations between layers. CKA computed using Gaussian RBF kernel (a = 1.0) on 2,500 samples of PTB-XC TTB-LL (all) dataset per model.
This result should be compared to the CKA comparison of the late features, which shown in Figure 5c The two transformer models (ECG-JEPA and ST-MEM) remain highly similar, whereas ECGFounder mains very dissimilar to all three other models. Also the dissimilarity of ECG-CPC and the transformer models increases in later layers, which supports the hypothesis that the ECG-CPC model learns distinet representations and does not replicate the patterns learned by the transformer models.
A.9.3 INSIGHTS
To summarize, the hybrid CNN-SSM model (ECG-CPC) the CNN encoder extracts local features while the S4 layers progressively build temporal abstractions. Each component serves distinct purpose, and you see representational change at each layer. The CNN-based ECGFounder shows smooth gradients, but still high similaritics across blocks SO-S4 indicate redundancies, which could possibly alleviated by decreasing the number of layers. Both considered transformer models show nearly identical representations in the middle 8-10 layers, which puts into question the efficiency of the archtitecture. It raises the question why transformer models show this particular pattern on ECG data specifically. The isolated BIkŌ in the case of ST-MEM indicate poor integration of encoder and further processing layers, which might explain the deficiencies in downstream performance observed for this model.
The inter model comparisons reveal strong similarities between the transformer models in spite of dif- ferent training objectives. The results indicate potential for ensembling of ECGFounder with any of the other models maximize diversity.
The CKA analysis provided hints for ST-MEM's weakness (early bottleneck) and ECG-CPC's strengths (efficient, purposeful layer utilization), which establishes CKA as useful diagnostic tool for ECG FMs.
A.10 AUROC vs. SPECIFICITY VS. -SORE
In this section, shed light alternative performance metrics beyond macro AUROC. The clinically most commonly used performance metrics such as sensitivity and specificity require the specification of decision threshold. While this decision should be taken based clinical desiderata in restrict for simplicity to a unsspeciicc threshold. More specifically, we adjust the decision threshold for every condition such sensitivity of 0.8 is reached. We then assess model performance according
manner, we
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macro-averaged specificity and Fi-score. Due its widespread use in the literature, base the experiment on the PTB-XL XL on the most finegrained level of the label hierarchy covering 71 unique labels. We compare the performance of foundation models after finetuning. The results are compiled in Figure
As non- non-trivial finding, the model ranking according to AUROC completely agrees with the model ranking according to specificity, as suggests that better overall discriminative power according to AUROC trans- lates into better model performance in terms of clinically relevant performance metrics sensitivity/speccifctty relevant operating points. The Fi-seo assesses only precision/positive predictive value as the re- call/sensitivity fixed to 0.8 for all models. The model ranking based Fi-score shows correlation with the ranking based on AUROC but does not fully agree. We see the agreement in terms of specificity the more crucial observation as the positive predictive value depends the prevalence of the respective conditions in the dataset, which, however, does not follow a proper population- distribution.
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Sama
3:04
0.8
AUROC
0.6-
BCG-OPC
ECc--E
ECGraadde
ECG PM
NERL
STARM
(a) AUROC
Spacificity by
5,62
3018
Specifiity
0.4
ECG-OPC
EtGromedr
EcoPM
HERE
STADM
(b) Specificity
by Model
0.944
0.30
0.25
Score
0.20
0.25
0.30
Ecoranndr
MoNL
STANEM
(c) Fj -core
Figure 6: Performance comparison of foundation models PTB-XL under the finetuning with linear head evaluation mode. Evaluated using (a) AUROC, (b) Specificity, and (c) F Fi-score at target sensitivity of 0.8. Higher values indicate better performance.
32


Reviews:
[{"review_id": "jQrW2lCW4R", "reviewer": "Reviewer_pB5i", "summary": "This paper conducts a systematic benchmark study evaluating multiple ECG foundation models under consistent experimental conditions.\nBy analyzing model performance across diverse datasets and task types, it provides quantitative insights into model behavior and practical guidance for model selection in clinical applications.\nThe work focuses on empirical benchmarking and analytical insights rather than proposing a new algorithmic contribution.", "questions": "N/A", "limitations": "", "rating": null, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "Comprehensive benchmarking: The study compares a wide range of state-of-the-art ECG models in a controlled setting, offering a fair and transparent evaluation of their relative strengths and weaknesses.\n\nData-driven analysis: The authors carefully distinguish model performance according to data characteristics and clinical task types, providing actionable insights for practical deployment.\n\nReproducibility and transparency: Experimental design and results are clearly documented, facilitating reproducibility and potential extensions by future researchers.", "weaknesses": "Limited theoretical novelty: The paper lacks a fundamental methodological or architectural innovation in representation learning, focusing instead on empirical evaluation.\n\nMisalignment with ICLR's thematic focus: While the study is rigorous, its emphasis on benchmarking and empirical analysis does not align closely with ICLR's focus on advances in representation learning and theory-driven contributions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "37qXgXF2Jl", "reviewer": "Reviewer_rWpe", "summary": "This manuscript presents a comprehensive benchmark evaluation of 8 ECG foundation models across 26 clinically relevant tasks which span seven categories, using 12 public datasets. The authors systematically compare these models under fine-tuning, frozen, and linear evaluation settings, and also analyze their scaling behavior with dataset size. Another key contribution is the introduction of ECG-CPC, a lightweight structured state-space model pretrained with contrastive predictive coding, which demonstrates strong performance despite its small size. The study highlights the heterogeneous performance of existing foundation models across different ECG tasks and provides insights into their label efficiency and representational quality.", "questions": "See the weaknesses section.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. This work addresses a significant gap in the literature by providing a large-scale, systematic benchmark of ECG foundation models across a diverse set of clinical tasks. The breadth of tasks and datasets evaluated is impressive and adds substantial value to the field.\n2. The experimental design is rigorous, with careful attention to evaluation protocols, statistical testing, and comparison against strong supervised baselines. The use of bootstrapping for significance testing and the inclusion of multiple evaluation modes (fine-tuning, frozen, linear) enhance the reliability of the results\n3. The introduction of ECG-CPC as a lightweight yet effective model is a notable contribution. Its strong performance despite a smaller parameter count suggests that model efficiency can be achieved without sacrificing accuracy, which is particularly relevant for clinical applications where computational resources may be limited.", "weaknesses": "1. While parameter counts are reported, practical clinical deployment hinges on more than just parameter size. Metrics such as inference speed (latency), GPU memory footprint during inference, and energy consumption are critical for real-world applications, especially in resource-constrained environments (e.g., bedside monitors, mobile apps). Including these metrics would greatly enhance the benchmark's utility for clinicians and developers aiming to deploy these models.\n2. Why do some large models (e.g., ST-MEM) underperform despite extensive pretraining? Is it an architecture-pretraining mismatch, overfitting, or other factors? A more in-depth analysis (e.g., probing experiments, representation similarity analysis, or ablations on pretraining components) would transform the benchmark from a performance leaderboard into a source of actionable design principles for future ECG foundation models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zbXZhpGx7E", "reviewer": "Reviewer_u5DN", "summary": "This paper presents a benchmark study that compares several ECG foundation models and supervised baselines (e.g., Net-1D, S4) across multiple public datasets and diverse tasks. The scope is broad, and the implementation effort is good. The benchmark itself has potential value for the *medical* community. That said, the contribution is primarily *engineering-oriented*, resembling a large-scale code integration effort rather than a scientific advance. I do not see any novel **modeling** ideas or **methodological** innovations. As a result, the conclusions boil down to *each model has its strengths*, which feels somewhat descriptive rather than insightful. \n\nOverall, while the benchmark could be useful as an implementation resource, the current version does not fully substantiate its claim as a comprehensive benchmark for ECG foundation models.", "questions": "- table 1 reports results that deviate from original papers (parameter), without explanation.\n\n- While Sec 4.2 provides engineering insights, its conclusion seems inconsistent with the main narrative of the paper, unless the authors merely wish to highlight that *each model/training framework can exhibit its own advantages*.\n\n- The benchmark compares models trained with completely different datasets, architectures, and strategies, making it impossible to tell which factors drive performance differences. To ensure fairness and interpretability, at least one unified training dataset (e.g., MIMIC-IV or HEEDB) should be used across models.\n\n- The authors mention that they propose the ECG-CPC model as an ECG foundation model, but in reality, the main innovation of this model lies in the training strategy, specifically using CPC. On the other hand, the S4 model is fundamentally a supervised learning model that does not use contrastive learning. The issue here is that the core difference between the two seems to be limited to the training strategy rather than the model architecture or task adaptability. This difference is insufficient to distinguish a foundation model from a pre-trained model or a supervised model. What exactly is the definition of an ECG foundation model in this paper? We recommend that the authors clarify the definition of an ECG foundation model in the paper and choose the evaluation baselines based on this standard. Generally, a foundation model should have zero-shot inference capabilities, broad task adaptability, and generalizability across tasks, rather than simply being a pre-trained model fine-tuned for specific tasks.\n\n-  The core value of foundation models lies in their emergent abilities obtained through large-scale pre-training, especially in terms of zero-shot and few-shot generalization in unknown scenarios. However, the evaluation framework in this paper mainly focuses on fine-tuning and frozen/linear probe. While the authors mention the limitation of the lack of out-of-distribution evaluation, if the main value of a model is realized through fine-tuning, it would be more appropriate to call it a pre-trained model. I suggest that the authors supplement their evaluation tasks with zero-shot and few-shot evaluations in unknown scenarios.\n\n- Several implementation details (layer-wise learning rate, 2.5 s cropping, sliding-window averaging) are described as *crucial*, yet their contribution is not quantified. Ablations are needed here.\n\n- Beyond dataset integration, the paper does not articulate specific research questions or hypotheses. As a result, it feels more like a large-scale reproduction project than a scientific study.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Unified code framework (code factory) is a promising contribution.\n- Task coverage is broad.\n- The small-sample scaling experiment (Sec. 4.2) adds engineering and evaluative value.", "weaknesses": "- The paper mainly reports AUC, which is insufficient for assessing clinical utility. More comprehensive performance metrics should be included.\n\n- S4 is quite old among state-space models.\n\n- Regarding **ssm**, however, the paper only reports the number of parameters, without including FLOPs, latency, or GPU memory usage.\n\n- I do not see any novel **modeling** ideas or **methodological** innovations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CNlxVdLiRd", "reviewer": "Reviewer_DBgK", "summary": "This paper presents a large-scale benchmark of electrocardiogram (ECG) foundation models (FMs) across 12 datasets and 26 downstream tasks, grouped into seven clinical categories (adult/pediatric interpretation, cardiac structure & function, cardiac/non-cardiac outcomes, acute care, and patient characteristics).\nThe authors compare eight public ECG FMs and two supervised baselines under three evaluation modes (finetuning, frozen, and linear evaluation), analyze statistical significance via bootstrapping, and report label-efficiency scaling curves.\n\nThe paper’s stated goal is to offer a unified benchmark for ECG foundation models and to assess how pretraining methodologies, model architectures, and dataset scales influence transfer performance.", "questions": "Distinguish between MAE-pretrain and MAE-metric throughout.\nVerify the validity of pretraining sample counts (9 M / 2.5 M); add explicit deduplication rules.\nEnsure consistent naming (e.g., ECG-FM vs ECGFM) and unit spacing (“mV”, “s”).", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Timely topic. Benchmarking ECG foundation models is both clinically and methodologically relevant as the field moves toward general physiological representation learning.\n2. Broad coverage. The benchmark spans a diverse set of tasks beyond pure diagnostic classification (e.g., echo structure and clinical outcomes), which is rare in existing works.\n3. Consistent evaluation setup. Use of three evaluation protocols and bootstrapped significance tests provides a reasonable basis for cross-model comparison.\n4. Meaningful observations. It identifies model-dependent task preferences (e.g., ECG-CPC performs best on non-interpretation tasks while ECG-JEPA dominates pediatric tasks).\n5. Practical insight. Findings on layer-wise learning rates and 2.5 s crops, are useful for the community and highlight training sensitivity in ECG models.", "weaknesses": "1.Writing quality / presentation.\n\t1) Background and Introduction repeat the same opening sentence and ECG definition .\n\t2) Numerous typographical and notation issues (“5 s” vs “5s”, \"z-normalization\" vs \"$z$-normalization\", two different MAE meanings, non-standard math syntax).\n\t3) Figures and tables lack consistent captions and unit notations. Overall readability is below ICLR standards.\n2.Reproducibility and implementation details.\n\t1) Key preprocessing steps (lead selection, resampling, normalization, filtering) and training hyper-parameters (batch size, epochs, scheduler, random seed) are missing.\n\t2) The “query attention head” used for frozen evaluation is not fully described and thus not reproducible.\n\t3) No code release or data manifest for pretraining datasets (especially the pretraining dataset d (9.1 M) and dataset e (2.5 M) claims is not convincible in Table 1) .\n3.Statistical rigor.\n\t1) Although bootstrapping is applied, effect sizes and confidence intervals are not consistently reported; rank-based summary figures hide variance and task-specific uncertainty.\n4.\tNo consideration of multi-modal or text-supervised ECG representations.\n5.\tInterpretation and insight depth.\n\t1) The discussion section remains descriptive; there is no mechanistic analysis of why certain models excel on specific task types.\n\t2) Visualization of representational spaces or layer-wise probing would strengthen claims about foundation knowledge .", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)