📋 AI Review from DeepReviewer will be automatically processed
📋 AI Review from ZGCA will be automatically processed
The paper proposes Adaptive Evidential Meta-Learning (AEML) for ECG personalization. A frozen ECG foundation model provides features; a lightweight evidential head outputs Dirichlet parameters for classification and uncertainty; and a hypernetwork conditions class-conditional priors from a few patient-specific samples using robust feature-space statistics (median and MAD) (Sec. 4.5–4.7; Eqs. 11–13). Training uses a two-stage meta-curriculum: Stage 1 on clean clinical tasks and Stage 2 on noisy, real-world variants (Sec. 4.6; Eq. 10), optimizing an evidential loss with KL regularization toward the hypernetwork-generated priors (Sec. 4.4; Eq. 6). Experiments across synthetic, clinical (MIT-BIH, CPSC2018), and wearable datasets compare against fine-tuning, LoRA, MAML, Proto/MatchNet, ECG-specific meta-learning, post-hoc calibration (temperature scaling, isotonic regression), and uncertainty baselines (MC Dropout, Ensembles) (Sec. 6.2). The method reports lower ECE, higher accuracy, improved OOD detection (using K / sum alpha as score), and better computational efficiency due to the frozen backbone (Sec. 6.4; Table 1). Limitations acknowledge the theoretical fragility of few-shot prior conditioning (Sec. 8).
Cross‑Modal Consistency: 16/50
Textual Logical Soundness: 12/30
Visual Aesthetics & Clarity: 12/20
Overall Score: 40/100
Detailed Evaluation (≤500 words):
1. Cross‑Modal Consistency
• Major 1: Reported accuracies conflict across text and figures. Evidence: Table 1 “Ours … Accuracy 90.1 ± 0.8” vs Fig. 1(a) and per‑dataset accuracy plots showing ≤0.60 (often ~0.3–0.4).
• Major 2: Core ECE comparison across methods is referenced but missing. Evidence: “Figure 6 presents ECE comparison across methods…” (Sec 6.4); no Fig. 6 provided.
• Major 3: Ablation contradicts claim. Evidence: Fig. 2(b) bars show Class‑Conditional ECE higher than Baseline for most datasets, while caption states it “significantly reduce[s] calibration error.”
• Major 4: Metric definition mismatch. Evidence: Sec 5: “conf = E[max_k p_k] = α_max/Σα_j” — E[max] ≠ max(E[p_k]); this affects all ECE numbers.
• Major 5: Loss term duplicated. Evidence: Eq. (6) already includes KL(Dir(α)||Dir(α0)); Eq. (14) adds “+ λ_KL · KL(Dir(α_i)||Dir(α0,i))” again.
• Minor 1: Architecture inconsistency. Evidence: Sec 4.1 “12‑layer convolutional neural network” vs Sec 4.2 “series of convolutional and recurrent layers.”
• Minor 2: Extra, unlabeled/supplementary plots (e.g., “Final ECE … (Baseline)” showing near‑zero ECE for most datasets) are not referenced and conflict with other figures.
2. Text Logic
• Major 1: Central claim of superior calibration lacks consistent, verifiable evidence. Evidence: Sec 6.4 claims “significantly lower calibration error (p<0.01)” but cross‑method figure is missing and ablation Fig. 2(b) suggests the opposite.
• Minor 1: Inference claims “single forward pass” while adaptation requires few‑shot statistics per patient; clarify per‑patient precomputation. Evidence: Sec 4.10 and Sec 4.9.
• Minor 2: Two‑stage curriculum described, but scheduling specifics (epochs/ratio/transition rule) absent. Evidence: Sec 4.6 provides ranges but no concrete schedule.
3. Figure Quality
• Major 1: Figure‑text identity/confusion in ablations; colors/legends suggest Baseline < Class‑Conditional, opposite to caption. Evidence: Fig. 2(b) bars and caption text in Fig. 2.
• Minor 1: Many small plots are duplicated/unindexed; numbering breaks flow (e.g., additional accuracy/loss panels without figure numbers).
• Minor 2: Some legends overlap content; small fonts on axis ticks in multi‑panel accuracy/loss plots hinder quick reading at print size.
Key strengths:
Key weaknesses:
Recommendations:
📋 AI Review from SafeReviewer will be automatically processed
This paper introduces Adaptive Evidential Meta-Learning (AEML), a framework designed to enhance ECG model personalization by incorporating uncertainty quantification. The authors propose a method that leverages a pre-trained ECG foundation model, keeping its parameters frozen, and attaches a lightweight evidential head. This head outputs parameters for a Dirichlet distribution, which is used to model both aleatoric and epistemic uncertainty. A key innovation is the use of a hypernetwork that generates the parameters for the evidential head's prior distribution, conditioned on robust, class-conditional statistics computed from a few patient-specific ECG samples. This allows the model to adapt to individual patient characteristics while maintaining computational efficiency. The training process is structured as a two-stage meta-curriculum, where the model first learns from high-quality clinical data and then adapts to noisy real-world data. The authors evaluate their approach on several datasets, including clinical, synthetic, and wearable ECG data, demonstrating improvements in Expected Calibration Error (ECE), accuracy, and out-of-distribution (OOD) detection compared to several baselines, including full fine-tuning, LoRA, and conventional meta-learning approaches. The paper's main contribution lies in the integration of evidential learning with a hypernetwork conditioned on patient-specific statistics within a meta-learning framework, specifically tailored for ECG personalization. The results suggest that the proposed method is effective in providing well-calibrated uncertainty estimates while maintaining high accuracy, which is crucial for clinical applications. The authors also provide a detailed analysis of the computational efficiency of their approach, showing that it achieves a good balance between performance and computational cost. The paper's focus on uncertainty calibration in the context of ECG personalization is a significant contribution, addressing a critical gap in existing methods. The two-stage meta-curriculum also addresses the challenge of domain shift, which is common in real-world clinical settings. Overall, the paper presents a well-motivated and empirically validated approach to ECG model personalization, with a focus on uncertainty awareness and computational efficiency.
I find several aspects of this paper to be particularly strong. The core idea of combining evidential learning with a hypernetwork conditioned on patient-specific statistics is a novel and promising approach to ECG model personalization. This integration allows for uncertainty-aware predictions while maintaining computational efficiency, which is a critical consideration for clinical applications. The use of a hypernetwork to generate priors for the evidential head, based on robust, class-conditional statistics, is a clever way to adapt the model to individual patient characteristics using only a few samples. This addresses a key limitation of many existing methods that require large amounts of patient-specific data for fine-tuning. The two-stage meta-curriculum training strategy is another strength of the paper. By first training on high-quality clinical data and then adapting to noisy real-world data, the model becomes more robust to domain shifts, which is a common challenge in real-world ECG analysis. The empirical results presented in the paper are compelling. The authors demonstrate significant improvements in Expected Calibration Error (ECE), accuracy, and out-of-distribution (OOD) detection capabilities compared to several baselines. These results are consistent across multiple datasets, including clinical, synthetic, and wearable ECG data, which strengthens the generalizability of the findings. The inclusion of a computational efficiency analysis is also a positive aspect of the paper. The authors show that their method achieves a good balance between performance and computational cost, making it suitable for real-time clinical deployment. The paper also includes ablation studies that demonstrate the contribution of each component of the proposed framework, which provides valuable insights into the effectiveness of the approach. Finally, the paper addresses a critical gap in existing methods by focusing on uncertainty calibration, which is often overlooked in ECG model personalization. This is a significant contribution, as well-calibrated uncertainty estimates are crucial for building trustworthy machine learning systems in healthcare.
Despite the strengths of this paper, I have identified several weaknesses that warrant further attention. First, the paper's reliance on a 5-shot learning scenario for adapting to new patients is a significant limitation. While the authors demonstrate the effectiveness of their approach in this setting, they do not adequately address scenarios where fewer than 5 samples are available, which is common in real-world clinical practice. The paper lacks a discussion on how the uncertainty estimates behave with fewer samples, and whether the model's confidence remains reliable. This is a critical issue, as the robustness of the uncertainty quantification with varying numbers of samples is essential for the practical applicability of the method. The paper also does not explore the impact of highly variable or noisy initial samples on the adaptation process. In real-world settings, the initial samples used for adaptation may not be representative of the patient's overall ECG profile, and the paper does not investigate how this affects the model's performance and uncertainty estimates. This is a crucial limitation, as the method's reliance on the quality of the initial samples could limit its robustness in clinical settings. My analysis also reveals that the paper's description of the OOD detection process is not entirely clear. While the paper states that the threshold for OOD detection is determined using the validation set, it does not provide specific details on how this threshold is set. The paper also lacks a clear explanation of how the OOD score is calculated and how it relates to the uncertainty estimates. This lack of clarity makes it difficult to fully understand the OOD detection process and its effectiveness. Furthermore, the paper's presentation of results in Figure 2 is not entirely clear. While the caption provides some explanation, the initial interpretation of the figure as an ablation study of different components of the proposed method is not immediately obvious. The paper could benefit from a more detailed explanation of the figure's content and the specific comparisons being made. Additionally, the paper's description of the baseline methods is not as detailed as it could be. While the paper mentions the baselines used, it does not provide sufficient information on their specific architectures and training procedures. This lack of detail makes it difficult to fully assess the novelty and contribution of the proposed method compared to existing approaches. The paper also lacks a detailed discussion of the specific challenges in ECG model personalization that motivated the development of the proposed method. While the paper mentions the limitations of existing methods, it does not provide a comprehensive analysis of the specific issues that the proposed method aims to address. This lack of context makes it difficult to fully appreciate the significance of the paper's contribution. Finally, the paper does not provide a detailed analysis of the impact of different types of noise on the model's performance. While the paper mentions the use of a noise model, it does not provide a specific analysis of how different noise types affect the accuracy and calibration of the model. This is a critical limitation, as the robustness of the method to different types of noise is essential for its practical applicability in real-world clinical settings. The paper also lacks a discussion of the potential impact of the hypernetwork's capacity on the results. While the authors mention the size of the hypernetwork, they do not explore the impact of different hypernetwork architectures or sizes on the performance of the proposed method. This is a crucial aspect that needs further investigation, as the hypernetwork's capacity could significantly affect the model's ability to adapt to different tasks and datasets. The paper also does not provide a detailed analysis of the computational cost of the proposed method compared to other meta-learning approaches. While the paper includes a computational efficiency analysis, it does not provide a comprehensive comparison with other meta-learning methods. This is a critical limitation, as the computational cost of the method is an important factor for its practical applicability. The paper also does not provide a detailed analysis of the model's performance on different types of ECG abnormalities. While the paper mentions the arrhythmia types considered, it does not provide a breakdown of the model's performance on each type. This is a crucial limitation, as the model's performance may vary across different types of abnormalities. Finally, the paper does not provide a detailed analysis of the model's performance on different patient populations. While the paper mentions the datasets used, it does not provide a breakdown of the model's performance on different patient groups. This is a critical limitation, as the model's performance may vary across different patient populations.
To address the identified weaknesses, I recommend several concrete improvements. First, the authors should conduct a more thorough investigation into the behavior of the uncertainty estimates with varying numbers of samples per class, particularly in low-data regimes. Specifically, they should evaluate the model's performance with 1, 2, and 3 samples per class, and analyze how the uncertainty estimates change in these scenarios. This analysis should include not only the Expected Calibration Error (ECE) but also visualizations of calibration curves and reliability diagrams to provide a more granular view of the model's confidence. Furthermore, it would be beneficial to explore the sensitivity of the model to the specific samples chosen for adaptation. For example, the authors could investigate how the uncertainty estimates vary when using different combinations of samples from the same patient, which would provide insights into the robustness of the adaptation process. To address the concern about the representativeness of the initial samples, the authors should conduct experiments using samples with significant morphological variations or those that are known to be noisy. This could involve manually selecting samples with atypical ECG patterns or introducing synthetic noise to the initial samples. The analysis should focus on how the model's uncertainty estimates respond to these challenging inputs. For instance, does the model exhibit higher uncertainty when presented with noisy or atypical samples, and does this uncertainty correlate with the actual error in prediction? Furthermore, the authors should explore strategies to mitigate the impact of unrepresentative initial samples, such as using a more robust statistic than the median for prior calculation or incorporating a mechanism to detect and down-weight unreliable initial samples during adaptation. The authors should provide a more detailed explanation of the OOD detection process, including the specific datasets used for OOD evaluation and the criteria for selecting these datasets. It is crucial to clarify how the threshold for OOD detection is determined using the validation set and how this threshold generalizes to unseen data. The authors should also provide a more detailed explanation of the figure's content and the specific comparisons being made in Figure 2. This could involve adding annotations to the figure or providing a more detailed description in the text. The authors should also provide more details on the baseline methods, including their specific architectures and training procedures. This would allow for a more comprehensive comparison of the proposed method with existing approaches. The authors should also provide a more detailed discussion of the specific challenges in ECG model personalization that motivated the development of the proposed method. This would provide a better context for the paper's contribution. The authors should also conduct a more detailed analysis of the impact of different types of noise on the model's performance. This analysis should include a comparison of the model's performance under different noise conditions and an investigation of the model's ability to detect OOD samples under noisy conditions. The authors should also explore the impact of the hypernetwork's capacity on the results by experimenting with different hypernetwork architectures and sizes. This would provide insights into the optimal hypernetwork configuration for the proposed method. The authors should also provide a more detailed analysis of the computational cost of the proposed method compared to other meta-learning approaches. This analysis should include a comparison of the training time, inference time, and memory usage of the proposed method with other meta-learning methods. The authors should also provide a more detailed analysis of the model's performance on different types of ECG abnormalities. This analysis should include a breakdown of the model's performance on each type of abnormality, as well as an analysis of the model's uncertainty estimates for each type. Finally, the authors should provide a more detailed analysis of the model's performance on different patient populations. This analysis should include a breakdown of the model's performance on different patient groups, as well as an analysis of the model's uncertainty estimates for each group.
Based on my analysis, I have several questions that I believe are important for further clarification. First, how does the model perform when only 1 or 2 samples are available for adaptation, and how do the uncertainty estimates behave in these low-data scenarios? This is a critical question, as the practical applicability of the method depends on its ability to perform well with limited data. Second, how does the model respond to initial samples that are highly variable or noisy, and does the uncertainty increase appropriately in these cases? This is important for understanding the robustness of the method in real-world clinical settings. Third, what is the specific method used to set the threshold for OOD detection, and how does this threshold generalize to unseen data? This is crucial for understanding the effectiveness of the OOD detection process. Fourth, what is the impact of different types of noise on the model's performance, and how does the model's ability to detect OOD samples change under noisy conditions? This is important for understanding the robustness of the method to different types of noise. Fifth, what is the impact of the hypernetwork's capacity on the results, and what is the optimal hypernetwork configuration for the proposed method? This is important for understanding the sensitivity of the method to the hypernetwork's architecture. Sixth, how does the computational cost of the proposed method compare to other meta-learning approaches, and what are the trade-offs between performance and computational cost? This is important for understanding the practical applicability of the method. Seventh, how does the model perform on different types of ECG abnormalities, and does the model's performance vary across different types of abnormalities? This is important for understanding the model's strengths and weaknesses. Finally, how does the model perform on different patient populations, and does the model's performance vary across different patient groups? This is important for understanding the model's generalizability and potential biases.