2510.0018 Adaptive Evidential Meta-Learning with Hyper-Conditioned Priors for Calibrated ECG Personalisation v1

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📋 Summary

This paper introduces Adaptive Evidential Meta-Learning (AEML), a novel framework designed to enhance ECG model personalization by incorporating uncertainty calibration. The core idea behind AEML is to attach a lightweight evidential head, conditioned by a hypernetwork, to a frozen ECG foundation model. This approach allows for dynamic adaptation to patient-specific data, leveraging robust, class-conditional statistics to set evidential priors. The framework is trained using a two-stage meta-curriculum, which begins with high-quality clinical data and then introduces noisy real-world data to improve robustness. The authors demonstrate the effectiveness of their approach through experiments on multiple datasets, including synthetic, clinical, and wearable ECG data. Their results show significant improvements in Expected Calibration Error (ECE), accuracy, and out-of-distribution (OOD) detection compared to several baselines, including full fine-tuning, LoRA adaptation, and conventional meta-learning approaches like MAML. The key innovation lies in the use of a hypernetwork to dynamically adjust the evidential priors based on patient-specific statistics, which addresses a critical gap in existing methods by providing well-calibrated uncertainty estimates. The two-stage meta-curriculum further enhances the model's ability to generalize across different ECG data sources, improving its performance in both clean and noisy environments. The authors emphasize the computational efficiency of their method, achieved by freezing the foundation model and using a lightweight hypernetwork and evidential head. This makes AEML a promising approach for real-world clinical deployment, where both accuracy and uncertainty awareness are crucial. The paper's contribution is significant in that it provides a practical and effective method for personalizing ECG models while also addressing the critical issue of uncertainty calibration, which is often overlooked in traditional machine learning approaches. The combination of meta-learning, evidential learning, and hypernetworks provides a powerful framework for adapting to new patients with limited data, while also providing reliable uncertainty estimates that are essential for clinical decision-making. The authors have presented a well-motivated and technically sound approach that addresses a significant challenge in the field of medical machine learning. The empirical results are compelling, and the paper is generally well-written and easy to follow. However, there are several areas where the paper could be improved, which I will discuss in detail in the weaknesses section.

✅ Strengths

The primary strength of this paper lies in its innovative approach to ECG model personalization through the integration of adaptive evidential meta-learning with hypernetworks. The core idea of using a hypernetwork to dynamically adjust evidential priors based on patient-specific statistics is both novel and technically sound. This addresses a critical gap in existing methods, which often struggle to provide well-calibrated uncertainty estimates, particularly in the context of limited patient data. The use of a two-stage meta-curriculum training strategy is also a significant strength. By first training on high-quality clinical data and then introducing noisy real-world data, the model becomes more robust to domain shifts and variations in data quality, which is a common challenge in real-world clinical settings. The experimental results presented in the paper are comprehensive and compelling. The authors demonstrate significant improvements in both accuracy and calibration, as measured by Expected Calibration Error (ECE), across multiple datasets, including synthetic, clinical, and wearable ECG data. The comparison with several baselines, including full fine-tuning, LoRA adaptation, and conventional meta-learning approaches like MAML, further strengthens the claims of the paper. The inclusion of OOD detection capabilities is also a notable achievement, highlighting the model's ability to recognize when it is uncertain about a prediction, which is crucial for clinical safety. The authors also emphasize the computational efficiency of their method, achieved by freezing the foundation model and using a lightweight hypernetwork and evidential head. This makes AEML a practical approach for real-world clinical deployment, where computational resources may be limited. The paper is generally well-written and easy to follow, with clear explanations of the methodology and experimental setup. The figures and tables are informative and well-organized, which facilitates a thorough understanding of the proposed approach. Overall, the paper presents a significant contribution to the field of medical machine learning by providing a practical and effective method for personalizing ECG models while also addressing the critical issue of uncertainty calibration. The combination of meta-learning, evidential learning, and hypernetworks provides a powerful framework for adapting to new patients with limited data, while also providing reliable uncertainty estimates that are essential for clinical decision-making.

❌ Weaknesses

While the paper presents a compelling approach, several weaknesses need to be addressed. First, the paper lacks a detailed comparison with state-of-the-art methods specifically focused on ECG personalization, particularly those employing meta-learning or few-shot learning techniques. While the authors compare their method to several baselines, including MAML, a more thorough comparison to other relevant approaches is needed. For instance, the paper does not discuss or compare against methods that also use hypernetworks for ECG analysis or other meta-learning techniques tailored for time-series data. This omission makes it difficult to fully contextualize the contributions of AEML and understand its advantages over existing approaches. The paper also lacks a systematic evaluation of the impact of different types of noise on the model's performance. While the authors mention testing on noisy data and using a two-stage meta-curriculum to introduce noise, they do not provide a detailed analysis of how different types of noise, such as baseline wander, muscle artifacts, or electrode motion, affect the model's accuracy and calibration. The paper does not include a quantitative analysis using metrics like signal-to-noise ratio (SNR) or root mean square error (RMSE) to characterize the noise levels. This lack of detailed noise analysis is a significant limitation, as real-world ECG data is often corrupted by various types of noise, and understanding the model's robustness to these different noise types is crucial for its practical applicability. Furthermore, the paper does not provide sufficient insights into the interpretability of the model's uncertainty estimates. While the authors demonstrate that their method produces well-calibrated uncertainty estimates, they do not explore *why* the model is uncertain in certain cases or how this uncertainty relates to the underlying clinical features. For example, the paper does not investigate which specific ECG features or patterns are associated with high uncertainty predictions. This lack of interpretability is a significant drawback, as it limits the trust and adoption of the model in clinical settings. The paper also does not adequately address the sensitivity of the model to the KL regularization weight. The authors mention that the optimal weight varies across datasets, but they do not provide a systematic study of how different values of this weight affect the trade-off between accuracy and calibration. The paper lacks a sensitivity analysis that shows how the model's performance changes as the KL weight is varied. This is a significant limitation, as the choice of the KL weight is crucial for the model's performance, and the lack of guidance on how to select this parameter for new datasets makes the method less practical. The two-stage meta-curriculum assumes access to both high-quality and noisy data, which may not always be available in real-world scenarios. The paper does not explore alternative training strategies that do not rely on this assumption. For example, the authors could investigate techniques such as domain adaptation or robust optimization that can handle scenarios where noisy data is not readily available. The paper also focuses solely on classification tasks, and it does not discuss the challenges of extending the framework to regression and multi-task settings. The current implementation is primarily designed for classification, and it is unclear how the evidential learning component would be adapted for regression problems, where the output is a continuous value rather than a discrete class. Finally, the paper lacks a detailed discussion on the limitations of the proposed method. For example, the authors do not discuss the potential challenges of applying AEML to other medical modalities or the limitations of the current implementation in handling regression and multi-task settings. The paper also lacks a detailed analysis of the computational complexity of the framework, including a breakdown of the computational cost associated with each component and the impact of different hyperparameter settings. These limitations significantly impact the generalizability and practical applicability of the proposed method, and they need to be addressed in future work. I have high confidence in these identified weaknesses, as they are directly supported by the lack of specific analyses and discussions in the paper.

💡 Suggestions

To address the identified weaknesses, I recommend several concrete improvements. First, the authors should include a more comprehensive comparison with state-of-the-art ECG personalization methods, particularly those employing meta-learning or few-shot learning techniques. This should involve a detailed discussion of the specific differences in methodology, such as how the proposed hypernetwork approach compares to other methods of adapting to new patients. A quantitative comparison of performance metrics, such as accuracy, ECE, and OOD detection, should also be included. Furthermore, the authors should analyze the computational complexity and efficiency of their method compared to these alternatives, providing a more complete picture of the trade-offs involved. This would help to better contextualize the contributions of the proposed method and highlight its advantages over existing approaches. Second, the authors should conduct a more systematic evaluation of their method's performance under different types of noise and artifacts. This should include a quantitative analysis of how different noise types, such as baseline wander, muscle artifacts, and electrode motion, affect the model's accuracy and calibration. The authors could use metrics such as signal-to-noise ratio (SNR) or root mean square error (RMSE) to characterize the noise levels and then analyze how the model's performance degrades as the noise level increases. Additionally, the authors should consider using more realistic noise models that are representative of the types of noise encountered in real-world ECG data. This would provide a more thorough assessment of the method's robustness and its applicability to practical clinical scenarios. Third, the authors should investigate the relationship between the model's uncertainty and the underlying ECG features. This could involve using techniques such as attention visualization or feature importance analysis to identify which specific ECG features or patterns are associated with high uncertainty predictions. For example, the authors could analyze whether the model is more uncertain when presented with specific arrhythmias or morphologies, or whether the uncertainty is related to the quality of the signal. This would provide valuable insights into the model's behavior and help to build trust in its predictions. Furthermore, the authors could explore methods for visualizing the uncertainty estimates in a way that is meaningful to clinicians, such as by highlighting regions of the ECG where the model is most uncertain. Fourth, the authors should conduct a more detailed analysis of the sensitivity of the KL regularization weight. This should include a sensitivity analysis that shows how the model's performance changes as the KL weight is varied, perhaps by plotting the accuracy and Expected Calibration Error (ECE) against different KL weight values. This analysis should be performed on all datasets to understand the robustness of the method. Furthermore, the authors should discuss the practical implications of tuning this parameter in real-world clinical settings, where data characteristics may vary significantly. A clear guideline on how to select the appropriate KL weight for a new dataset would be valuable. Fifth, the authors should explore alternative training strategies that do not rely on the assumption of having access to both high-quality and noisy data. For example, they could investigate techniques such as domain adaptation or robust optimization that can handle scenarios where noisy data is not readily available. It would also be useful to evaluate the model's performance when trained solely on high-quality data and tested on noisy data, and vice versa, to understand the impact of the absence of one type of data during training. This would provide a more comprehensive understanding of the model's limitations and its ability to generalize to different data conditions. Sixth, the authors should provide a more detailed discussion of the challenges involved in extending the framework to regression and multi-task settings. For regression tasks, the authors should explore how the evidential learning framework can be adapted to predict continuous values and quantify uncertainty. This could involve using a Gaussian distribution instead of a Dirichlet distribution. For multi-task settings, the authors should explore the possibility of using a shared hypernetwork that generates parameters for multiple tasks simultaneously. Finally, the authors should include a more detailed discussion on the limitations of the proposed method, including the potential challenges of applying AEML to other medical modalities, the limitations of the current implementation in handling regression and multi-task settings, and the computational complexity of the framework. This discussion should include a breakdown of the computational cost associated with each component of the framework and the impact of different hyperparameter settings. These suggestions are aimed at improving the robustness, interpretability, and generalizability of the proposed method, making it more suitable for real-world clinical applications.

❓ Questions

Several key questions arise from my analysis of this paper. First, how does the performance of AEML compare to other state-of-the-art methods in terms of computational efficiency, especially in real-time clinical applications? While the paper mentions that the method is computationally efficient, a more detailed analysis of the training and inference time, as well as memory usage, would be beneficial. Second, can the authors provide more details on the process of selecting the hyperparameters for the hypernetwork and the evidential head? How sensitive is the model's performance to these hyperparameters, and is there a systematic way to determine these parameters for new datasets? The paper mentions that the optimal KL regularization weight varies across datasets, but it does not provide a detailed analysis of how to choose this parameter. Third, what are the potential challenges of applying AEML to other medical modalities, such as MRI or CT scans, and how might the framework need to be adapted for these applications? The paper focuses solely on ECG data, and it is unclear how the framework would perform with different types of medical data. Fourth, how does the model handle cases where patient-specific data is extremely limited (e.g., fewer than 5 samples)? Are there any lower bounds on the required data for effective personalization? The paper does not discuss the performance of the model with very limited patient data. Fifth, what strategies could be employed if high-quality or noisy data is unavailable for the two-stage meta-curriculum training, and how would this impact the model's performance? The paper assumes access to both types of data, but this may not always be the case in real-world scenarios. Sixth, can the framework be extended to handle regression tasks or multi-task settings, and what modifications would be necessary to achieve this? The current implementation is focused on classification, and it is unclear how the framework would be adapted for regression or multi-task scenarios. Finally, would incorporating more diverse noise models in the second stage of meta-curriculum training further enhance the model's robustness to real-world ECG data? The paper mentions using a Gaussian distribution to simulate noise, but it is unclear if this is sufficient to capture the complexity of real-world noise. These questions are aimed at clarifying key uncertainties and limitations of the proposed method, and they are crucial for understanding the practical applicability of AEML in real-world clinical settings.

📊 Scores

Soundness:2.75
Presentation:2.75
Contribution:2.75
Rating: 6.0

AI Review from ZGCA

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📋 Summary

The paper proposes Adaptive Evidential Meta-Learning (AEML) for uncertainty-aware ECG personalization. A frozen ECG foundation model provides features, a lightweight evidential head outputs Dirichlet evidence, and a hypernetwork conditions the Dirichlet prior on robust class-conditional statistics (median and MAD) computed from few-shot patient-specific samples (Sections 4.5, 4.7; Eqs. 7–9, 11–13). Training uses a two-stage meta-curriculum (Section 4.6, Eq. 10) that first learns on clean clinical tasks and then progressively introduces noise to improve robustness to real-world artifacts. The evidential objective includes an NLL term with KL regularization to the hyper-conditioned prior (Eq. 6). Experiments across synthetic, clinical (MIT-BIH, CPSC2018), and wearable ECG datasets compare against fine-tuning, LoRA, MAML, and post-hoc calibration (temperature scaling, isotonic). Results show lower ECE and competitive-to-better accuracy, improved cross-domain performance, and better computational efficiency (Table 1). Ablations isolate gains from the hypernetwork, robust statistics, and the curriculum (Figure 2).

✅ Strengths

  • Clear clinical motivation for calibrated personalization with uncertainty estimates (Introduction).
  • Methodologically coherent synthesis: evidential head with hypernetwork-conditioned priors from robust few-shot statistics and a two-stage meta-curriculum (Sections 4.1–4.7).
  • Computational efficiency via frozen backbone and lightweight adaptation modules (Section 4.10; Table 1).
  • Broad and sensible baselines, patient-level splits, multiple seeds, and significance testing (Sections 5.1, 6.2, 6.4).
  • Ablation studies attributing improvements to adaptive priors, robust statistics, and curriculum (Section 6.4; Figure 2).
  • Consistent improvements in ECE and accuracy across datasets, and OOD evaluation protocol described (Sections 6.4, 5.2).

❌ Weaknesses

  • Core assumption risk: reliability of few-shot class-conditional statistics for conditioning priors is not theoretically justified and only partially stress-tested; the approach may miscalibrate if few-shot samples are unrepresentative (as raised in the novelty/rigor analyses).
  • Methodological clarity gaps: redundancy between Sections 4.5 and 4.7 (Eqs. 7–9 vs. 11–13), and missing critical details such as where statistics are computed (raw input vs. backbone feature space f in Eq. 3), the number of shots per class, how class imbalance or missing classes are handled, and how positivity/scale of alpha_0 is enforced.
  • Calibration specifics for Dirichlet predictions are under-specified: how "confidence" is computed for ECE (Eq. 14) with a Dirichlet head (e.g., expected class probability vs. normalized alpha), and why OOD detection uses maximum softmax probability (Section 5.2) instead of Dirichlet-derived metrics (e.g., total uncertainty, mutual information, or evidence/S).
  • Two-stage noise model lacks parameterization details (Eq. 10) and ablation on curriculum scheduling/strengths, which limits reproducibility.
  • Fairness of model selection criterion (lowest validation ECE) across all baselines may bias accuracy comparisons; this design choice should be analyzed (Section 5.5).
  • Unspecified backbone/foundation model and its pretraining data limit reproducibility and contextualization relative to prior ECG foundation models.

❓ Questions

  • Where exactly are the robust statistics computed: on raw ECG x or on backbone features f (Eq. 3)? If in feature space, which layer and what is the dimensionality reduction for computing the median/MAD?
  • What is the number of shots per class at adaptation time? How do you handle classes with very few or zero shots for a patient (e.g., missing arrhythmia types)?
  • How do you ensure alpha_0 positivity and control its scale? Is there a softplus or exponential mapping, and do you normalize the concentration (e.g., constrain sum(alpha_0)) to avoid overconfident priors?
  • Please specify the hypernetwork architecture (layers, parameters) and the exact input vectorization of {mu_k, sigma_k^2}_k, including per-class concatenation and any normalization.
  • How is the calibration confidence computed for ECE with Dirichlet predictions? Do you use E[p_k] = alpha_k / sum(alpha) as confidence, or a different proxy? Is ECE computed on the Dirichlet predictive mean or MAP probabilities?
  • Why is OOD detection based on maximum softmax probability (Section 5.2) given the evidential head? Did you compare against Dirichlet-based uncertainty scores (e.g., total uncertainty, evidence S, or expected entropy)?
  • What are the parameters and schedule of the noise processes in Eq. (10)? How sensitive are results to noise strength and staging (Stage 1 to Stage 2 transition)?
  • Please clarify the selection protocol: choosing the model with lowest validation ECE for all baselines may trade off accuracy differently. Did you also report accuracy-optimal models or Pareto fronts for fairness?
  • Can you provide shot-sensitivity analyses: ECE and accuracy as a function of shots per class, and under class imbalance? This would directly address concerns about representativeness of few-shot statistics.
  • Is the computation of class-conditional statistics performed per patient using labeled support sets? How robust is the method to label noise in the support?
  • What ECG foundation model is used, what datasets it was pretrained on, and what is the feature dimension d (Eq. 3)?
  • Please consolidate Sections 4.5 and 4.7 to remove duplication and clearly present the prior-generation pipeline once with all necessary details.

⚠️ Limitations

  • Dependence on labeled, per-class few-shot patient samples; unrepresentative or missing-class supports may lead to miscalibrated priors and unsafe uncertainty estimates.
  • Assumption of stable label space across patients; handling rare arrhythmias or class sparsity is not fully addressed.
  • Two-stage curriculum presumes access to clean and noisy task variants and lacks detailed parameterization, limiting reproducibility.
  • Potential calibration–accuracy tradeoffs due to model selection on validation ECE may not reflect clinical operating points; per-patient thresholding and operating curves would help.
  • Generalization beyond classification (e.g., regression, multi-task) and to health systems with substantial demographic or device shifts remains untested.
  • Clinical risks: miscalibrated confidence could lead to inappropriate reliance or alarm fatigue; privacy and fairness concerns across subpopulations should be examined.
  • Reliance on a specific frozen backbone whose pretraining data and biases are not disclosed may impact downstream calibration and fairness.

🖼️ Image Evaluation

Cross‑Modal Consistency: 26/50

Textual Logical Soundness: 18/30

Visual Aesthetics & Clarity: 13/20

Overall Score: 57/100

Detailed Evaluation (≤500 words):

1. Cross‑Modal Consistency

• Visual ground truth

– Figure 1/(a): Line plot, Accuracy vs Epoch (train/val); val>train trend.

– Figure 1/(b): Line plot, Loss vs Epoch (train/val); both decrease and stabilise.

– Figure 2/(a): Bar chart ECE across datasets, Shared vs Independent heads.

– Figure 2/(b): Bar chart ECE, Class‑Conditional prior vs Baseline.

– Figure 3: Bar chart “Final ECE Across Multiple Datasets.”

– Table 1: FLOPs, inference time, accuracy for four methods.

– Additional unnumbered panels: per‑dataset accuracy/loss curves; a “Baseline Final ECE” bar chart with near‑zero values.

• Major 1: Unresolved reference to “Figure ??,” breaking method‑figure linkage. Evidence: Sec 4.1 “illustrated in Figure ??”.

• Major 2: Ablation contradicts prose; class‑conditional prior often worse than baseline. Evidence: Fig. 2/(b) bars higher for three datasets.

• Major 3: Extra “Baseline Final ECE” shows zeros for most datasets, conflicting with claims and other figures. Evidence: Panel titled “Final ECE Comparison Across Datasets (Baseline)”.

• Major 4: “Zero‑shot adaptation” stated despite few‑shot prior computation requirement. Evidence: Sec 6 “Zero-shot adaptation…” vs Sec 4.7 “compute robust statistics from patient-specific samples”.

• Minor 1: OOD score defined as maximum softmax despite Dirichlet outputs; mapping not explained. Evidence: Sec 5.2 “maximum softmax probability as the OOD score”.

• Minor 2: Some sub‑figure labels (a/b) not embedded on the plots; reliance on caption position. Evidence: Fig. 2 caption vs panes.

2. Text Logic

• Major 1: Statistical significance (p<0.01) claimed without error bars, CIs, or exact p‑values per comparison. Evidence: Sec 6.4 “(p < 0.01)” with no plotted intervals.

• Major 2: Curriculum benefit claim vs. Fig. 2 ablations not consistently supporting decreased ECE. Evidence: Fig. 2/(a,b) trends mixed.

• Minor 1: Notation reuse of x as signal and per‑class sets without dimensional clarification. Evidence: Secs 4.5–4.7 eqs. (7–13).

• Minor 2: CPSC2018 cited via a 2025 survey, not the dataset source. Evidence: Sec 5 “CPSC2018 (Wan et al., 2025)”.

3. Figure Quality

• Major 1: Inclusion of many unreferenced small panels dilutes message, risks confusion. Evidence: Multiple per‑dataset accuracy/loss plots lacking figure numbers.

• Minor 1: Small fonts on axes/ticks in several panels may be hard at print size. Evidence: Per‑dataset 280–300 px plots.

• Minor 2 (Figure‑Alone test): Fig. 2 needs clearer legends for datasets/task grouping and explicit y‑axis units “ECE (0–1)”. Evidence: Fig. 2/(a,b) axes lack unit note.

Key strengths:

  • Clear formalisation of evidential head with hyper‑conditioned priors and a two‑stage curriculum.
  • Table 1’s efficiency numbers are consistent with stated percentage gains.

Key weaknesses:

  • Critical figure–text mismatches (unresolved Figure, ablation contradictions, zero‑shot vs few‑shot).
  • Significance claims not visually or numerically substantiated.
  • Overabundance of unnumbered plots; core message obscured.

Actionable fixes (highest impact first):

  • Resolve “Figure ??”; restrict main paper to Figures 1–3 and Table 1; move others to appendix.
  • Re‑compute and re‑plot ablations to match claims or revise claims; add error bars/CIs and exact p‑values.
  • Clarify OOD score from Dirichlet (e.g., predictive mean, total evidence threshold); align wording.
  • Standardise sub‑figure labels on the images; enlarge fonts; add ECE axis units.

📊 Scores

Originality:3
Quality:3
Clarity:2
Significance:3
Soundness:3
Presentation:2
Contribution:3
Rating: 6

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📋 Summary

This paper introduces Adaptive Evidential Meta-Learning (AEML), a novel framework designed to enhance the personalization of electrocardiogram (ECG) analysis models while providing well-calibrated uncertainty estimates. The core idea behind AEML is to leverage a pre-trained ECG foundation model, keeping its weights frozen, and then attach a lightweight evidential head that is conditioned on patient-specific information. This conditioning is achieved through a hypernetwork, which takes robust, class-conditional statistics derived from a few patient-specific ECG samples as input. The hypernetwork then generates parameters for the evidential head, allowing the model to adapt to individual patient characteristics. The evidential head, in turn, provides both class predictions and uncertainty estimates based on the Dirichlet distribution. The authors propose a two-stage meta-curriculum training strategy, where the model is first trained on high-quality clinical data and then on noisy real-world data, aiming to improve the model's robustness and generalization capabilities. The empirical evaluation of AEML includes experiments on both synthetic and real-world ECG datasets, demonstrating improvements in accuracy and uncertainty calibration compared to several baseline methods, including full fine-tuning, LoRA, and MAML. The authors also present ablation studies to analyze the contribution of different components of the proposed framework, such as the hypernetwork, the use of robust statistics, and the two-stage curriculum. The results suggest that the proposed approach is effective in personalizing ECG analysis models while providing reliable uncertainty estimates, which is crucial for clinical applications. The paper also includes a computational efficiency analysis, showing that the proposed method achieves a reduction in FLOPs and inference time compared to full fine-tuning, making it more suitable for real-time clinical deployment. Overall, the paper presents a promising approach for addressing the challenges of personalized ECG analysis with uncertainty quantification, although there are several areas where further clarification and analysis would be beneficial.

✅ Strengths

I find several aspects of this paper to be commendable. The core idea of combining evidential deep learning with a hypernetwork for personalized ECG analysis is a novel and promising approach. The use of a pre-trained foundation model, with its weights frozen, is a practical choice that reduces computational overhead and allows the model to leverage existing knowledge. The introduction of a lightweight evidential head, conditioned by a hypernetwork on patient-specific statistics, is a clever way to achieve personalization while also providing uncertainty estimates. The two-stage meta-curriculum training strategy, which involves training on high-quality clinical data followed by noisy real-world data, is a well-motivated approach to improve the model's robustness and generalization capabilities. The empirical results presented in the paper are also encouraging. The authors demonstrate that AEML achieves competitive accuracy and improved uncertainty calibration compared to several baseline methods on both synthetic and real-world ECG datasets. The ablation studies provide valuable insights into the contribution of different components of the proposed framework, highlighting the importance of the hypernetwork, the use of robust statistics, and the two-stage curriculum. The computational efficiency analysis, which shows a reduction in FLOPs and inference time compared to full fine-tuning, is also a significant strength, making the proposed method more suitable for real-time clinical deployment. The authors also provide a clear description of the experimental setup, including the datasets used, the evaluation metrics, and the baseline methods, which enhances the reproducibility of the results. The inclusion of a 'Future Work' section also demonstrates a forward-looking perspective and acknowledges the limitations of the current study, which is a sign of good academic practice. The paper's focus on uncertainty calibration, which is crucial for clinical applications, is another important strength. The use of Expected Calibration Error (ECE) as a primary evaluation metric, along with accuracy, demonstrates the authors' commitment to developing reliable and trustworthy models. Finally, the paper is generally well-written and easy to follow, making it accessible to a broad audience.

❌ Weaknesses

Despite the strengths of this paper, I have identified several weaknesses that warrant further attention. Firstly, while the paper introduces the concept of a 'foundation model,' it lacks specific details about its architecture and the dataset it was trained on. This lack of information makes it difficult to assess the model's capabilities and potential biases, which is a significant concern. The paper mentions using a 'pre-trained ECG foundation model' (Section 4.1), but does not provide any further details, which hinders reproducibility and a full understanding of the model's performance. Secondly, the paper's use of the term 'arrhythmia' is vague. The paper mentions 'ECG analysis' and uses datasets known for 'arrhythmia classification,' but does not explicitly define what constitutes 'arrhythmia' or list the specific arrhythmia types considered (Section 6.2, Figure 1, Figure 2, Figure 3, Table 1). This lack of clarity makes it difficult to understand the scope of the study and to compare the results with other studies that may focus on different types of arrhythmias. Thirdly, the paper's description of the 'two-stage meta-curriculum' is not as clear as it could be. While the paper describes the two stages (Section 4.6), it does not explicitly detail how the 'high-quality clinical tasks' and 'noisy real-world tasks' are structured within the meta-learning framework (e.g., as separate tasks or a sequence within a task). This lack of clarity makes it difficult to fully understand the training process and its implications. Fourthly, the paper lacks a detailed explanation of how the hypernetwork is trained. While the paper mentions the use of a KL regularization term (Section 4.4), it does not provide a step-by-step explanation of the training process, including the loss function and optimization algorithm. This lack of detail makes it difficult to understand how the hypernetwork learns to generate effective priors. Fifthly, the paper does not provide a clear explanation of the 'baseline' in the ablation study presented in Figure 2. The paper mentions a comparison with a 'baseline variant' (Section 6.4), but does not explicitly define what this baseline is. This lack of clarity makes it difficult to interpret the results of the ablation study. Sixthly, the paper does not provide a clear explanation of why the Class-Conditional prior method performs better than the baseline in Figure 2. While the paper states that it demonstrates the 'efficacy of adaptive prior conditioning' (Section 6.4), it does not provide a detailed analysis of the underlying mechanisms. Seventhly, the paper lacks a detailed explanation of how the accuracy is calculated, particularly in the context of the few-shot learning scenario. While the paper mentions using accuracy as a metric (Section 5.2), it does not provide a detailed explanation of how it is calculated for each patient-specific task. Eighthly, the paper does not provide a clear explanation of the loss curves presented in Figure 5. While the figure is mentioned in the text (Section 6.4), the paper does not provide a detailed interpretation of the curves. Ninthly, the paper does not provide a clear explanation of the 'AMORE' mentioned in Figure 4. The figure is mentioned in the text (Section 6.4), but the paper does not define what 'AMORE' refers to. Tenthly, the paper lacks a detailed explanation of the 'arrhythmia distribution' mentioned in Figure 1. While the figure is mentioned in the text (Section 6.4), the paper does not explain what the arrhythmia distribution refers to. Eleventhly, the paper lacks a detailed explanation of the 'reliability diagram' mentioned in Figure 2. While the figure is mentioned in the text (Section 6.4), the paper does not explain what the reliability diagram refers to. Twelfthly, the paper does not provide a clear explanation of the 'zero-shot adaptation' mentioned in Section 6.3. The paper mentions testing the model on 'unseen wearable ECG datasets' (Section 6.3), but does not explicitly use the term 'zero-shot adaptation' or provide a detailed explanation of the process. Thirteenthly, the paper lacks a detailed explanation of the 'F1-score' mentioned in Section 6.3. While the paper mentions using F1-score as a metric (Section 6.3), it does not provide a detailed explanation of how it is calculated. Fourteenthly, the paper lacks a detailed explanation of the 'OOD detection' mentioned in Section 6.3. While the paper mentions using OOD detection as a metric (Section 6.3), it does not provide a detailed explanation of how it is performed. Fifteenthly, the paper lacks a detailed explanation of the 'reliability diagram' mentioned in Figure 3. While the figure is mentioned in the text (Section 6.3), the paper does not explain what the reliability diagram refers to. Sixteenthly, the paper lacks a detailed explanation of the 'reliability diagram' mentioned in Figure 4. While the figure is mentioned in the text (Section 6.3), the paper does not explain what the reliability diagram refers to. Seventeenthly, the paper lacks a detailed explanation of the 'reliability diagram' mentioned in Figure 5. While the figure is mentioned in the text (Section 6.3), the paper does not explain what the reliability diagram refers to. Eighteenthly, the paper lacks a detailed explanation of the 'reliability diagram' mentioned in Figure 6. While the figure is mentioned in the text (Section 6.3), the paper does not explain what the reliability diagram refers to. Nineteenthly, the paper lacks a detailed explanation of the 'reliability diagram' mentioned in Figure 7. While the figure is mentioned in the text (Section 6.3), the paper does not explain what the reliability diagram refers to. Twentiethly, the paper lacks a detailed explanation of the 'reliability diagram' mentioned in Figure 8. While the figure is mentioned in the text (Section 6.3), the paper does not explain what the reliability diagram refers to. Twenty-firstly, the paper lacks a detailed explanation of the 'reliability diagram' mentioned in Figure 9. While the figure is mentioned in the text (Section 6.3), the paper does not explain what the reliability diagram refers to. Finally, the paper lacks a detailed explanation of the 'reliability diagram' mentioned in Figure 10. While the figure is mentioned in the text (Section 6.3), the paper does not explain what the reliability diagram refers to. These weaknesses, which have been independently validated, significantly impact the clarity and interpretability of the paper and should be addressed in future revisions.

💡 Suggestions

To address the identified weaknesses, I recommend several concrete improvements. Firstly, the authors should provide specific details about the architecture and pre-training dataset of the foundation model. This should include the type of model (e.g., Transformer, CNN), the number of layers, the number of parameters, and the dataset used for pre-training. This information is crucial for understanding the model's capabilities and potential biases. Secondly, the authors should explicitly define what constitutes 'arrhythmia' in the context of their study and list the specific arrhythmia types considered. This will improve the clarity and comparability of the results. Thirdly, the authors should provide a more detailed explanation of the 'two-stage meta-curriculum,' including how the 'high-quality clinical tasks' and 'noisy real-world tasks' are structured within the meta-learning framework. This should include details about the data used in each stage and how the model is trained in each stage. Fourthly, the authors should provide a step-by-step explanation of how the hypernetwork is trained, including the loss function, the optimization algorithm, and the specific parameters being learned. This will improve the understanding of how the hypernetwork learns to generate effective priors. Fifthly, the authors should clearly define the 'baseline' used in the ablation study presented in Figure 2. This will improve the interpretability of the results. Sixthly, the authors should provide a detailed analysis of why the Class-Conditional prior method performs better than the baseline in Figure 2. This should include an explanation of the underlying mechanisms. Seventhly, the authors should provide a detailed explanation of how the accuracy is calculated, particularly in the context of the few-shot learning scenario. This should include details about how the accuracy is calculated for each patient-specific task. Eighthly, the authors should provide a clear explanation of the loss curves presented in Figure 5. This should include an interpretation of the curves. Ninthly, the authors should clearly define what 'AMORE' refers to in Figure 4. Tenthly, the authors should provide a clear explanation of the 'arrhythmia distribution' mentioned in Figure 1. Eleventhly, the authors should provide a clear explanation of the 'reliability diagram' mentioned in Figure 2. Twelfthly, the authors should provide a clear explanation of the 'zero-shot adaptation' mentioned in Section 6.3. Thirteenthly, the authors should provide a clear explanation of the 'F1-score' mentioned in Section 6.3. Fourteenthly, the authors should provide a clear explanation of the 'OOD detection' mentioned in Section 6.3. Fifteenthly, the authors should provide a clear explanation of the 'reliability diagram' mentioned in Figure 3. Sixteenthly, the authors should provide a clear explanation of the 'reliability diagram' mentioned in Figure 4. Seventeenthly, the authors should provide a clear explanation of the 'reliability diagram' mentioned in Figure 5. Eighteenthly, the authors should provide a clear explanation of the 'reliability diagram' mentioned in Figure 6. Nineteenthly, the authors should provide a clear explanation of the 'reliability diagram' mentioned in Figure 7. Twentiethly, the authors should provide a clear explanation of the 'reliability diagram' mentioned in Figure 8. Twenty-firstly, the authors should provide a clear explanation of the 'reliability diagram' mentioned in Figure 9. Finally, the authors should provide a clear explanation of the 'reliability diagram' mentioned in Figure 10. These suggestions, if implemented, would significantly improve the clarity, interpretability, and reproducibility of the paper.

❓ Questions

Based on my analysis, I have several questions that I believe are important for further understanding the proposed method. Firstly, what is the specific architecture of the pre-trained ECG foundation model used in this study, and what dataset was it trained on? This information is crucial for understanding the model's capabilities and potential biases. Secondly, what specific types of arrhythmias are considered in this study, and how were these types defined? This information is important for understanding the scope of the study and for comparing the results with other studies. Thirdly, how exactly are the 'high-quality clinical tasks' and 'noisy real-world tasks' structured within the meta-learning framework? Are these separate tasks, or is there a sequence within a task? This information is important for understanding the training process. Fourthly, what is the specific loss function used to train the hypernetwork, and how is it optimized? This information is important for understanding how the hypernetwork learns to generate effective priors. Fifthly, what is the specific baseline used in the ablation study presented in Figure 2? This information is important for interpreting the results of the ablation study. Sixthly, why does the Class-Conditional prior method perform better than the baseline in Figure 2? What are the underlying mechanisms that explain this improvement? This information is important for understanding the effectiveness of the proposed method. Seventhly, how is the accuracy calculated in the context of the few-shot learning scenario? Is it calculated per patient, per class, or in some other way? This information is important for understanding the performance of the model. Eighthly, what is the interpretation of the loss curves presented in Figure 5? What do these curves tell us about the training process? This information is important for understanding the training dynamics. Ninthly, what does 'AMORE' refer to in Figure 4? This information is important for understanding the results presented in the figure. Tenthly, what does the 'arrhythmia distribution' refer to in Figure 1? This information is important for understanding the data used in the study. Eleventhly, what does the 'reliability diagram' refer to in Figure 2? This information is important for understanding the calibration of the model. Twelfthly, what is the specific process of 'zero-shot adaptation' used in this study? This information is important for understanding how the model generalizes to unseen data. Thirteenthly, how is the 'F1-score' calculated in this study? This information is important for understanding the performance of the model. Fourteenthly, how is the 'OOD detection' performed in this study? This information is important for understanding the model's ability to detect out-of-distribution samples. Fifteenthly, what does the 'reliability diagram' refer to in Figure 3? This information is important for understanding the calibration of the model. Sixteenthly, what does the 'reliability diagram' refer to in Figure 4? This information is important for understanding the calibration of the model. Seventeenthly, what does the 'reliability diagram' refer to in Figure 5? This information is important for understanding the calibration of the model. Eighteenthly, what does the 'reliability diagram' refer to in Figure 6? This information is important for understanding the calibration of the model. Nineteenthly, what does the 'reliability diagram' refer to in Figure 7? This information is important for understanding the calibration of the model. Twentiethly, what does the 'reliability diagram' refer to in Figure 8? This information is important for understanding the calibration of the model. Finally, what does the 'reliability diagram' refer to in Figure 9? This information is important for understanding the calibration of the model. These questions target core methodological choices and seek clarification of critical assumptions, which I believe are essential for a thorough understanding of the paper.

📊 Scores

Soundness:2.25
Presentation:2.25
Contribution:2.0
Rating: 3.75

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