PONE-D-24-31262Trustworthy diagnosis of Electrocardiography Signals based on Out-of-Distribution DetectionPLOS ONE

Dear Dr. Yu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Rajesh N V P S Kandala, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: After carefully reading the article, here is my review:

Article Title: Trustworthy Diagnosis of Electrocardiography Signals Based on Out-of-Distribution Detection

This article generally has a well-structured writing style and adequately explains the research findings. However, the overall novelty of the article is low, as it mainly presents the integration of existing techniques. The experiments conducted are also insufficient to support the claims of novelty in this research.

Detailed Analysis of the Article:

1. Novelty

Assessment: Poor While the article presents an interesting combination of techniques for trustworthy ECG diagnosis, its novelty is limited as it primarily relies on integrating existing techniques. The main contribution lies in the application of this combination to the ECG domain, but it does not offer significant methodological advancements or deep new insights. Here are some detailed reasons why the novelty of this research is considered low:

• Use of Existing Techniques: The article essentially combines several previously established techniques:

o CNN and Attention Mechanism: The use of CNN for feature extraction from ECG signals and attention mechanisms to enhance representation learning is a well-established approach in the literature.

o Energy Score and ReAct: Although the use of the Energy score for OOD detection and ReAct for handling overconfidence is relatively new, both techniques have already been explored in other contexts.

• Direct Application to ECG: While the combination of these techniques for ECG diagnosis may not have been done before, the article does not provide strong evidence that this application requires significant methodological innovation. It is more of a direct application of existing techniques to the ECG domain.

• Lack of Comparison with Other OOD Methods: The article mainly focuses on comparisons with traditional Softmax-based methods. A more comprehensive comparison with other state-of-the-art OOD detection methods in the ECG domain would strengthen the novelty claims.

• Limited Focus on Anomaly Detection: Although OOD detection is important, the article primarily emphasizes identifying anomalies (unknown heart diseases). A more significant contribution could be made if the method also addressed the enhancement of known heart disease classification or offered new insights into OOD results interpretation.

2. Problem Backgrounds

Assessment: Inadequate The article provides a generally clear background on the importance of ECG diagnosis and the challenges faced, particularly in detecting unknown heart diseases. However, there are some shortcomings in the problem background in the introduction:

• Lack of Depth in Existing Challenges: The article mentions that current deep learning methods struggle with OOD heart diseases but does not deeply explain why this occurs. A better understanding of the underlying technical challenges, such as domain shifts or lack of representative training data, would enrich the problem background.

• Limited Focus on Anomaly Detection: While OOD detection is important, the article mainly focuses on anomaly identification. The problem background could be expanded by discussing the importance of accurate classification for known heart diseases and how the inability to handle OOD cases can affect the overall trustworthiness of the diagnosis.

• Weak Connection between the Problem and the Proposed Solution: The article could better explain how the proposed method specifically addresses the challenges identified in the problem background. A more explicit connection between the problem and the proposed solution would strengthen the narrative coherence of the article.

• Lack of a Broader Literature Review: Although the article cites several previous studies, a broader literature review on OOD detection efforts in ECG analysis would provide better context about the current state of research and highlight the gaps this study aims to fill.

3. Method Used is Clearly Defined

Assessment: Fairly Good The "Proposed Method" section outlines the methodology used in the research. While it provides a general overview of the approach, there are areas where the explanation could be improved to provide a clearer and more comprehensive understanding.

• Lack of Coherence and Clear Structure: The explanation in this section sometimes feels fragmented and lacks a clear logical flow. Transitions between paragraphs and ideas could be improved to enhance readability and understanding.

• Insufficient Technical Explanation: Some technical aspects of the proposed method are not adequately explained. For example:

o CNN-Attention Architecture: Although this architecture is mentioned, there is no detailed explanation of how CNN and attention mechanisms are integrated. More information about specific layers, parameters, and how they contribute to feature extraction would be beneficial.

o Energy and ReAct: The explanation of these techniques is quite technical and may be difficult for readers unfamiliar with the concepts. Illustrative examples or analogies could help explain the core principles more accessibly.

o Threshold for OOD Detection: How is the threshold for the Energy score determined? A further explanation of this process and how the threshold affects the model’s performance would improve understanding of the OOD detection mechanism.

• Lack of Justification for Design Choices: The article could better explain why certain design choices were made. For example, why was ResNet-18 chosen as the base model? Why were specific intermediate layers selected for feature extraction? Such justification would strengthen the methodological foundation of the research.

• Stronger Connection to the Problem Background: This section could better explain how the proposed method specifically addresses the challenges identified in the problem background. A more explicit connection between the problem and the proposed solution would improve the narrative coherence of the article.

4. Experimental Results

The experimental results presented in Table 3 are insufficient to support the claims of novelty in this research. Here are some reasons for this:

• Limited Baseline: The article mainly compares the proposed method with traditional Softmax-based approaches. While this comparison is useful, it is not sufficient to demonstrate significant novelty. It would be more convincing if the method were compared with other state-of-the-art OOD detection methods that have been applied to ECG analysis. This would show whether the proposed method offers significant performance improvements over other recent approaches.

• Evaluation on a Single Dataset: Experiments are only conducted on a single dataset (MIT-BIH Arrhythmia Database). Although this dataset is widely used, evaluation on multiple datasets from various sources would provide stronger evidence of the generalization and robustness of the proposed method. This would demonstrate whether the method can perform well in a variety of real-world scenarios.

• Limited Focus on Performance Metrics: Table 3 only presents results for a few common performance metrics (AUROC, AUPR, F1-score, etc.). A more comprehensive analysis would include other metrics relevant to OOD detection, such as false detection rates and misclassification rates on OOD samples.

• Lack of Ablation Analysis: The article does not include an ablation study to evaluate the contribution of each component (CNN-Attention, Energy, ReAct) to the overall performance. Ablation analysis would help understand the importance of each component and whether the specific combination proposed is necessary to achieve the reported results.

• Lack of Robustness Analysis: The article does not explore how the proposed method's performance is affected by various factors, such as noise levels in ECG signals or class imbalance in the dataset. Robustness analysis would provide insights into the limitations of the method and its potential for application in challenging clinical settings.

5. Critical Analysis

Assessment: Inadequate The article presents an analysis of the experimental results, including comparison with baseline methods and visualization. However, there are some deficiencies in the critical analysis of the experimental results:

• Lack of In-Depth Discussion of Results: The article reports improved performance on evaluation metrics, but it does not provide an in-depth analysis of why the proposed method leads to these improvements. A more detailed discussion of how each component of the method (CNN-Attention, Energy, ReAct) contributes to performance improvements would be valuable.

• Unacknowledged Limitations: The article does not adequately discuss potential limitations of the proposed method. For example, how does the method perform on OOD heart disease types that are very different from those in the training dataset? Or how is the performance affected by variations in ECG signal quality?

• Lack of Qualitative Comparison: The article focuses on quantitative comparison with baseline methods. Qualitative analysis, such as case studies or examples of misclassifications, would provide richer insights into the strengths and weaknesses of the proposed method.

• Unclear Clinical Implications: The article mentions potential clinical applications but does not discuss in detail how the method could be integrated into clinical workflows or its potential impact on patient diagnosis and treatment.

• Lack of Sensitivity Analysis: The article does not explore how the method’s performance is influenced by various parameters or design choices. Sensitivity analysis would help understand the robustness and generalizability of the proposed method.

6. Conclusion

Assessment: Fairly Good The conclusion summarizes the main findings of the research and highlights the potential of the proposed method for trustworthy ECG diagnosis. Additionally, the authors acknowledge the limitations of the current research and outline future research directions, indicating a good understanding of the scope of their work.

7. Recent References Is Used (Five Years from Now)

Assessment: Excellent The article cites many recent references (within the last five years), demonstrating that the authors have reviewed the latest literature and placed their work in the context of current research. This adds credibility and relevance to their research.

8. Figures and Tables in the Manuscript Clear and Readable?

In general, the figures and tables in this manuscript are clear and readable, but there are some aspects that can be improved to enhance their presentation quality.

Positive Aspects:

• Readability: The text in the figures and tables is generally clear and easy to read, with an adequate font size.

• Relevance: The figures and tables directly support the content of the text and provide relevant visual illustrations or data summaries.

• Captions: The figures and tables have captions that provide context and explain their content.

Aspects that Could Be Improved:

• Image Quality: Some images, particularly flowcharts (such as Figure 2), could benefit from improved resolution to make them sharper and clearer, especially when zoomed in.

• Consistency of Format: The format of the tables could be more consistent, particularly in the use of lines and spacing. Ensuring uniformity in spacing and line usage will improve the readability and professional appearance of the tables.

• Numbering: There appears to be some inconsistency in the numbering of figures and tables in certain parts of the manuscript. Ensuring consistent numbering will improve the organization and cross-referencing within the text.

• Visual Quality: Some visualizations, such as the confidence distribution in Figure 7, could be enhanced with better color choices or other graphic design elements to improve clarity and visual appeal.

• • Connection with the Text: While the figures and tables are relevant, the explanations in the text could sometimes be more explicit in connecting the visual content with the main arguments or points being discussed.

Reviewer #2: My comments are as follows:

1. The language throughout the text can be improved. There are several grammar mistakes and, most importantly, some sentences are unclear.

2. Abstract does not highlight novelty of the proposed work. It’s better to add more specific details of your work.

3. Introduction is not focused and literature can be reorganised to strengthen literature review following contributions and discuss few relevant works for both CNN and ECG i.e.,

-- Unsupervised pre-trained filter learning approach for efficient convolution neural network

-- A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

-- ModPSO-CNN: an evolutionary convolution neural network with application to visual recognition

-- CSFL: A novel unsupervised convolution neural network approach for visual pattern classification

-- Optimisation‐based training of evolutionary convolution neural network for visual classification applications

-- Optimization of CNN through novel training strategy for visual classification problems

-- Face recognition: A novel un-supervised convolutional neural network method

-- Design optimization of electromagnetic devices using an improved quantum inspired particle swarm optimizer

-- A quantum particle swarm optimizer with enhanced strategy for global optimization of electromagnetic devices

4.Figure 1 is hard to follow, for ease of the reader its highly recommended to add more specific detail of the proposed approach.

5. Organise your results some are difficult to understand e.g., what do you want to conclude in Figure 8.

Reviewer #3: This work employs a deep learning (Attention and CNN) based techniques with Energy and ReAct techniques to to recognize OOD heart diseases. Although the approach is entirely not new, this is an interesting study. However, there is lot of ambiguity in the paper.

What is IT in the introduction?

Firstly, the dataset (in dataset introduction) used in this work needs to be elaborated. Like the number of classes in the dataset and number of samples per each class.

Data enhancement: Authors have written "Applied selectively". Did they use this step selectively on each segment or subject selectively? More clarity is required.

Also elaborate the data augmentation ion detail. Give the details about the parameters used in this process. The statistics of data before and after data augmentation. This is necessary to reproduce the results.

Experimental results: "To balance the sample distribution, appropriate down-sampling methods were employed.' authors should detail how it is done.

Also, the accuracy is below 85%. Authors should discuss on where their approach failed.

Authors have compared their approach with [35]. There are many other approaches. I suggest the authors to make a comparison with recent approaches.

Please check the references, References needs to be properly formatted. Eg ref 36.

Authors should change the title of able 3.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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PONE-D-24-31262R1Trustworthy diagnosis of Electrocardiography Signals based on Out-of-Distribution DetectionPLOS ONE

Dear Dr. Yu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR: 

• Please do any changes that are requested by the reviewers. 

==============================

Please submit your revised manuscript by Jan 24 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Rajesh N V P S Kandala, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Partly

Reviewer #4: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: I Don't Know

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: Yes

Reviewer #4: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

Reviewer #4: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: (No Response)

Reviewer #4: 1 - In the latest version of the manuscript, It is unclear how the threshold for ReAct is chosen. In an earlier version it appears the threshold is selected by a simple heuristic involving known ID and OOD samples. Is this still the case?

2 - If indeed this is the case, then how well does the proposed method work when the threshold is determined using one set of ID and OOD samples, and is faced with a different set of OOD samples not seen during the threshold selection process?

3 - Are the results obtained averaged over multiple runs (with different random network initializations, for instance) ? It is unclear how the results are obtained and aggregated.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #3: No

Reviewer #4: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

Trustworthy diagnosis of Electrocardiography Signals based on Out-of-Distribution Detection

PONE-D-24-31262R2

Dear Dr. Yu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Rajesh N V P S Kandala, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: