Classification of current density vector map using transformer hybrid residual network

Lihui Zhu; Yunfeng Yang; Wenyue Yu; Qingxin Zeng; Zhan Zheng; Qiang Lin; Xiaohu Zhao; Zhenghui Hu

doi:10.1371/journal.pone.0338189

Peer Review History

Original SubmissionMarch 4, 2025
13 Oct 2025 Decision Letter - Agnese Sbrollini, Editor PONE-D-25-09738Classification of Current Density Vector Map Using Transformer Hybrid Residual NetworkPLOS ONE Dear Dr. Hu, 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. Please submit your revised manuscript by Nov 27 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. 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If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] 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: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: I Don't Know ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? 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Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 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: 1.Several instances in the manuscript show multiple references cited within a single sentence without clear separation. Please ensure each reference is distinctly and correctly formatted. 2.Avoid using keywords such as “we”, “us” and “you” in the manuscript. Academic writing should maintain a formal and objective tone throughout. 3.The following reference appears to be incomplete or unverifiable: “31. He X, Huang Y, Li S, Su S, Zheng W, Hu Z, Lin Q. Measurement of cardiac magnetic field based on atomic magnetometry. Chinese Journal of Medical Physics, 2017.” Please provide a valid citation with complete bibliographic details or evidence of its availability. 4.The results claim 100% accuracy, which may appear unrealistic in practical scenarios. Kindly justify this outcome and explain how further research or improvement is still relevant in the context of your proposed model. Reviewer #2: This paper proposes a novel deep learning model that combines a ResNet-18 backbone with a Transformer encoder (ResNet-TL-TE) to classify Magnetocardiogram-derived Current Density Vector Maps (CDVMs) into five clinical categories. To overcome a small original dataset, the authors employed data augmentation techniques including noise addition, autoregressive modeling, and interpolation. The model utilizes transfer learning from a pretrained resnet on ImageNet and leverages Transformers to capture global dependencies in the CDVM data. The proposed method achieves a high test accuracy of 97.52%, outperforming baseline benchmark. The paper is generally well-written and easy to follow. The abstract is well-structured and summarizes the paper effectively. It could be slightly improved by explicitly mentioning the “outperforming previous approaches like ResNet-LSTM” The introduction is excellent. It successfully establishes the clinical importance of MCG and CDVM, clearly identifies the existing challenges (data scarcity, judgment difficulty), and logically leads to the need for a computer-aided method. The literature review is adequate and relevant. It covers early works (k-NN), recent deep learning approaches (MCG-Net), and related works using ResNet/Transformers in cardiac analysis The methodology is mostly thorough. The data preprocessing, augmentation strategies, and dataset partitioning are explained in great detail. The rationale for choosing ResNet-18, transfer learning, and the Transformer encoder is well-justified. The training strategy (hyperparameter optimization, optimizer, overfitting prevention) is clearly described. The results are internally consistent. The ablation study (ResNet < ResNet-TE < ResNet-TL < ResNet-TL-TE) logically shows the incremental benefit of each component. The high performance on the test set is consistent with the strong validation performance. Possible limitations of the study: Given the very small original dataset, even if the augmentation is extensive and the subject-wise split is correct, the final model's performance, however impressive, must be viewed with caution regarding its generalizability to entirely new populations and clinical settings. The authors correctly acknowledge this as a limitation. The class distribution is also highly imbalanced (e.g., Category 4 vs. Category 0), which, while perhaps reflective of reality, can bias a model. The high specificity scores might be influenced by this. The conclusion is well-written. It succinctly restates the main achievement (the proposed model and its SOTA accuracy), summarizes the significant contributions, and, importantly, acknowledges the key limitations (small original dataset, potential for architectural optimization). The future directions (larger datasets, lightweight models) are sensible and appropriate. It effectively closes the paper without overstating the findings. Minor comment: The figures are well-prepared and visually clear. However, captions are missing — each figure currently includes only a title. Figures should be self-contained, with detailed captions that describe all subpanels, symbols, and relevant details to ensure clarity without referring back to the main text. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). 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Revision 1
10 Nov 2025 Author Response Responses Dear Reviewers: We would like to thank 2 anonymous reviewers for their insightful suggestions and valuable comments, which helped to improve the manuscript. We have revised our paper, and the related changes to the manuscript are highlight with red text color. And we have responded to the Reviewers' comments point by point as follows: Response to the Reviewer 1 Q1: Several instances in the manuscript show multiple references cited within a single sentence without clear separation. Please ensure each reference is distinctly and correctly formatted. Response: We sincerely thank the reviewer for the careful observation regarding citation formatting. In response, we have thoroughly reviewed the manuscript and ensured that multiple references cited within a single sentence are clearly separated and correctly formatted according to the journal’s style. All relevant changes have been implemented throughout the text. Q2: Avoid using keywords such as “we”, “us” and “you” in the manuscript. Academic writing should maintain a formal and objective tone throughout. Response: We appreciate the reviewer’s comment on maintaining a formal and objective tone. We have carefully revised the manuscript with the following actions: 1.Replaced first-person expressions with impersonal, objective phrasing. 2.Employed passive voice or process-oriented wording in Methods and Results where appropriate to reduce subjectivity. 3.Removed second-person forms and replaced them with neutral nominal phrases or structured statements when necessary. These changes have been implemented consistently across the manuscript, in line with the journal’s style requirements. Q3: The following reference appears to be incomplete or unverifiable:“31. He X, Huang Y, Li S, Su S, Zheng W, Hu Z, Lin Q. Measurement of cardiac magnetic field based on atomic magnetometry. Chinese Journal of Medical Physics, 2017.”Please provide a valid citation with complete bibliographic details or evidence of its availability. Response: We thank the reviewer for pointing out the incomplete citation. The full bibliographic details and evidence of availability have now been provided, and the reference list has been updated accordingly (with in-text citations synchronized). The DOI is: 10.3969/j.issn.1005-202X.2017.11.016. The manuscript has been revised to ensure compliance with the journal’s reference style. Q4: The results claim 100% accuracy, which may appear unrealistic in practical scenarios. Kindly justify this outcome and explain how further research or improvement is still relevant in the context of your proposed model. Response: We thank the reviewer for the insightful comment on the classification accuracy. We fully agree that the 100% precision and specificity in certain categories require clarification, and we appreciate the opportunity to elaborate on their rationality and subsequent improvement directions. 1.Rationality of the Result �The 97.52% in the manuscript refers to the overall average accuracy, while 100% only applies to the precision and specificity of Category 1 (normal pattern) and Category 2 (mild abnormality) (see Table 2), not the overall accuracy across all categories. �Clinical features of Category 1 and 2 are significantly distinguishable from other categories (regular current distribution patterns for normal/mildly abnormal cases). Combined with ResNet’s local feature extraction and Transformer’s global dependency modeling (Section 2.4), the model can accurately capture these clear classification boundaries. 2.Future Research and Improvements �Optimize class balance: Addressing the current imbalance (Category 4 with the highest proportion: 2235 samples; Category 0 with the lowest: 290 samples), we will adopt weighted loss functions and stratified oversampling strategies to balance the model’s learning weight for each category. �Expand dataset scale: We plan to collect more MCG data, which will cover more complex pathological types (e.g., coronary heart disease of varying severity and arrhythmia), to reduce the selection bias caused by the small sample size. Response to the Reviewer 2 Q1: The figures are well-prepared and visually clear. However, captions are missing — each figure currently includes only a title. Figures should be self-contained, with detailed captions that describe all subpanels, symbols, and relevant details to ensure clarity without referring back to the main text. Response: We sincerely thank the reviewer for this helpful comment. We have carefully revised all figures and added informative captions so that each figure is self-contained. �Fig 1. CDVM imaging framework Processing of raw MCG signals and imaging of the current density vector map �Fig 2. Cardiac magneto signal A single-channel cardiac magneto signal waveform over one cardiac cycle, illustrating the typical morphology with P wave, QRS complex, J point, ST segment, and T wave. �Fig 3. Multichannel MCG data Multichannel MCG data from 49 channels. �Fig 4. Generation of current density vector map Raw 49-channel MCG signals were preprocessed (denoising and baseline correction) to plot the magnetic field map, and the current density vector map was reconstructed over the ST–T segment with a 5ms sampling interval. �Fig 5. Data enhancement Example of an augmented CDVM generated by noise addition, ARIMA prediction, and interpolation. �Fig 6. Classification system of CDVM Category 0–1 denote normal patterns, category 2 indicates mild abnormality, and category 3–4 signify severe abnormalities. Representative examples for each category are shown. �Fig 7. Network structure schematic diagram The model consists of a ResNet-18 backbone for local feature extraction, a Transformer encoder for global context modeling, and classification heads for CDVM classification. Data preprocessing and augmentation are applied prior to training; arrows indicate the forward flow. �Fig 8. Network architectures performance comparison Performance of CNN, MLP, and ResNet18. CNN/MLP val 51.95% (CNN train 51.53%); ResNet18 train 82.82%, val 83.41%.; ResNet18 performs notably better. �Fig 9. Hyperparameter optimization results Bayesian optimization explored 30 configurations across learning rate, weight decay, dropout, model dimension (d_model), number of layers, and feedforward dimension, with nhead fixed at 8. �Fig 10. ResNet18-LSTM vs ResNet18-Transformer Encoder The proposed model peaks at 94.81% validation accuracy at epoch 26, outperforming the ResNet-LSTM network (92.06%), with steadily decreasing validation loss and early stopping, indicating better temporal pattern modeling by the Transformer and no overfitting. �Fig 11. Transfer learning and Transformer encoder components ablation studies results ResNet: 83.41% val. accuracy; +TL: 92.35%; +TE (no TL): 85.57%; full ResNet‑TL‑TE: 94.81% with the lowest, most stable validation loss (0.155). TL accelerates convergence via better initialization, while TE improves temporal modeling and training stability. �Fig 12. Confusion matrix of test set The ResNet‑TL‑TE model attains an average accuracy of 97.52% on CDVM classification. The matrix visualizes per‑category predictions and errors, revealing near‑perfect diesscrimination for Categories 1–2 and balanced performance across all categories. �Table 1. Optimal hyperparameters configuration The best validation accuracy (92.35%) was obtained with a shallow-but-wide transformer setting. �Table 2. Precision, Recall, and Specificity for Each Category Categories 1–2 reach 100% precision and specificity; Category 0 has 100% recall; other Categories are high overall. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0338189.r002
20 Nov 2025 Decision Letter - Agnese Sbrollini, Editor Classification of Current Density Vector Map Using Transformer Hybrid Residual Network PONE-D-25-09738R1 Dear Dr. Hu, 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. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support. 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, Agnese Sbrollini Academic Editor PLOS ONE Additional Editor Comments (optional): 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 #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 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 #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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 #1: Yes Reviewer #2: Yes ****** 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 #1: Yes Reviewer #2: Yes ****** 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 #1: Multiple references are cited at one place; thats not a good practice. Other commments are addressed Reviewer #2: I am satisfied with the revisions. The figures now have self-contained captions as suggested. I recommend accepting the paper. ****** 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 #1: Yes: Dr. Meenu Gupta Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0338189.r003
Formally Accepted
Acceptance Letter - Agnese Sbrollini, Editor PONE-D-25-09738R1 PLOS One Dear Dr. Hu, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Agnese Sbrollini Academic Editor PLOS One https://doi.org/10.1371/journal.pone.0338189.r004

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