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Peer-Review Record

Multi-Scale Heart Beat Entropy Measures for Mental Workload Assessment of Ambulant Users

Entropy 2019, 21(8), 783; https://doi.org/10.3390/e21080783
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Entropy 2019, 21(8), 783; https://doi.org/10.3390/e21080783
Received: 26 June 2019 / Revised: 7 August 2019 / Accepted: 8 August 2019 / Published: 10 August 2019
(This article belongs to the Special Issue Multiscale Entropy Approaches and Their Applications)

Round 1

Reviewer 1 Report

Dear Editor,

I read the manuscript and I think that the authors performed a really good work. The research is well performed, the methodology is robust, and the results are interesting. 

The practical application of such results will give a valid methods for evaluating mental stress both in ambulant users and other workers.

Author Response

Thank you for your comments.


Reviewer 2 Report

different lengths of HRV signals can give slightly different entropy values, the correlation between the length of signals and the computed (followed) parameters could be a good completion of this study

Author Response

Comment: different lengths of HRV signals can give slightly different entropy values, the correlation between the length of signals and the computed (followed) parameters could be a good completion of this study


Reply: We have focused on the 5 minute RR time series segments as they are a standard approved by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996).


The longer segments can be analyzed for looking at effects on multi-scale entropy values, however, this decreases the number of data-points available for further analysis which could lead to issues in training the machine learning classifiers. Hence, we focused on different solutions proposed for improvement of estimation of entropy for a fixed time period.

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