PONE-D-21-10240

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

PLOS ONE

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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: No

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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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: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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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: No

Reviewer #2: Yes

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: Dear authors, it was pleasure reading the paper titled "Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1". I have some suggestions/comments for you to address. Please find the attached file for comments and suggestions.

Reviewer #2: The first objective of this paper is to establish a large and open-access respiratory sound database for training such an algorithm for the detection of breath phase and adventitious sounds, mainly focusing on lung sounds. The second objective is to conduct a benchmark test on the established lung sound database by using eight recurrent neural network-based models. (Eight RNN models namely LSTM, GRU, BiLSTM, BiGRU, CNN-LSTM, CNN-GRU, CNN-BiLSTM and CNN-BiLSTM).

Strengths:

- Clear description of the problematic (prolonged continuous monitoring of respiratory sound is almost impractical, the interpretation of auscultation results depends on the subjectivity of the practitioners, the importance of data size for creating robust and accurate DL models…).

-The results and the data are well presented.

Weaknesses:

The paper is well written, However, during the review process, I found few queries enlisted below that may be helpful to improve the quality of the paper with proper revision.

- Subsection “3.6 Task definition and evaluation metrics” line 293 [4] clearly —> need a proper citation.

- Line 433 “double triggering” —> “a double triggering”.

- It would be good to have a table that lists the abbreviations.

- The authors need to explain the eight recurrent neural network models.

- The title of subsection “3.2 Preprocessing”, “3.7 Hardware and software”, and “2.4 Data labeling” are badly placed in the text.

- The title of Table 1 is badly placed.

- The authors need to improve the quality of Figure 2, Figure 3, Figure 3, Figure 6, and Figure 7.

- The authors need to provide more details on the simplified version of the RNN-based models.

- “The filtered signals were then processed using the short-time Fourier transform (STFT)”, The authors need to explain the STFT method.

- The authors need to add a related work section to clearly position their work comparing to others.

Reviewer #3: 1. In Introduction section, the drawbacks of each conventional technique should be described clearly.

2. Introduction needs to explain the main contributions of the work more clearly.

3. The authors should emphasize the difference between other methods to clarify the position of this work further.

4. The Wide ranges of applications need to be addressed in Introductions

5. The objective of the research should be clearly defined in the last paragraph of the introduction section.

6. Fig 6 quality should be increased

7. The authors can cite the following references

Early detection of diabetic retinopathy using PCA-firefly based deep learning model

An ensemble based machine learning model for diabetic retinopathy classification

Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: PONE-D-21-10240_reviewer.pdf

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

PONE-D-21-10240R1

Dear Dr. Huang,

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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Kind regards,

Thippa Reddy Gadekallu

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 #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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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 #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: 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 #2: The paper has been improved significantly.

In general, the paper is well sounded, and the comparative analysis of the eight recurrent neural network variants is interesting.

Reviewer #3: The authors have addressed all of my comments. The paper can can be accepted in the current format. Thank you

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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 #2: No

Reviewer #3: No