Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis

Round 1

Reviewer 1 Report

1. The paper is too long and most of them can be shorten but in a letter way

2. What is the creativity of the paper? You discribed too much issues the readers already known;

3. The dataset of Guilin's hospital is a public open soure data? If not, how you get them and why no chinese co-authors? 

Author Response

Re: Response to reviewers

Dear Reviewer 01,

Here is our response regarding your valuable advice. We upload our point-by-point responses to comments (responses for reviewers). Submission changes are marked with a yellow highlight.

We thank you for your valuable suggestions and advice to improve our manuscript.

 

Best regards,

Ernia Susana, et al

Author Response File: Author Response.pdf

Reviewer 2 Report

In this paper, the authors present a novel method for non-invasive blood glucose monitoring using photoplethysmography waveform analysis. It was found that the PPG waveform differs between healthy and diabetic patients. BGL prediction was made by processing PPG signals with machine learning algorithms.

 

The manuscript is overall well written. I have some comments that the authors are suggested in consider.

Attached file with comments.

Comments for author File: Comments.pdf

Author Response

Re: Response to reviewers

Dear Reviewer 02,

Here is our response regarding your valuable advice. We upload our point-by-point responses to comments (responses for reviewers). Submission changes are marked with a yellow highlight.

We thank you for your valuable suggestions and advice to improve our manuscript.

 

Best regards,

Ernia Susana, et al

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

All my comments have been fixed and it can be accepted to be published.