Reviewer has chosen **not** to be *Anonymous*

**Overall Impression:** Good

**Suggested Decision: ** Undecided

**Technical Quality of the paper:** Average

**Presentation:** Weak

**Reviewer`s confidence:** Medium

**Significance:** Moderate significance

**Background:** Incomplete or inappropriate

**Novelty:** Limited novelty

**Data availability:** All used and produced data (if any) are FAIR and openly available in established data repositories

**Length of the manuscript:** The length of this manuscript is about right

**Summary of paper in a few sentences: **

The paper present a novel application of cycle representatives of topological features in constructing predictor variables for use in machine learning models to classify ECG rhythms. They introduce an isoelectric baseline to yield non-trivial topological features corresponding to the P, Q, S, and T-waves (if they exist) and utilize the N-most persistent 1-dimensional homological features and their corresponding area-minimal cycle representatives to construct predictor variables derived from the persistent homology of the ECG signal for some choice of N.

**Reasons to accept: **

The paper is well structured and presents indeed an interesting application in ECG.

With some efffort the paper can be accepted.

1) The SoA of application of Topological Data Analysis in Cardiovascular Signals is missing

-Hernández-Lemus, E., Miramontes, P., & Martínez-García, M. (2024). Topological Data Analysis in Cardiovascular Signals: An Overview. Entropy, 26(1), 67.

-Liu, Y., Wang, L., & Yan, Y. (2023, October). Persistence Landscape-based Topological Data Analysis for Personalized Arrhythmia Classification. In 2023 IEEE 19th International Conference on Body Sensor Networks (BSN) (pp. 1-6). IEEE.

Ling, T., Zhu, Z., Zhang, Y., & Jiang, F. (2022). Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal. Applied Sciences, 12(20), 10370.

and many others studies should be analysed, compared against your work .

2) Line 23 : pls use the same number of digits.

3) If your focus is on Arrythmia, you sd give the prevalence of Arrythmia

4) there is a typo : "off of"

5) The difference between homology and homotopy is not clear

6) Some data is discarded: in relation to the real tiem series what does it mean ? Large Amplitude? High/Low Frequency ?

7) One page to describe well known evaluation metrics is too much, can you condense pag 10

8) Why do you think Gradient Boosted Decision Tree results as optimal ? A nd what about the other results ? Results are just reported and not commented at all

9) The standard deviation of the metrics across the 5 folds could be reported in the tables for completeness. it would be useful to highlight best results in bold in the tables

10) Fig. 6. Receiver Operator Characteristic Curve for Classification of Arrhythmia vs. Sinus Rhythm, presents very bad results, what is the reason of presenting it ?

11) A Table to compare your results with other studies that approach ECG rhythm classification through TDA and machine learning, would be recommended.

12) page 16 line 11 , there is a typo "nonempty"

In general the application of Topological Data Analysis in Cardiovascular Signals as in all Time Series, is interesting, could you give an easier explanation of what is the meaning of this approach based on shape in ECG ? Graphs/Images could be helpfuls

**Reasons to reject: **

The paper is well structured and presents indeed an interesting application in ECG.

With some efffort the paper can be accepted.

1) The SoA of application of Topological Data Analysis in Cardiovascular Signals is missing

-Hernández-Lemus, E., Miramontes, P., & Martínez-García, M. (2024). Topological Data Analysis in Cardiovascular Signals: An Overview. Entropy, 26(1), 67.

-Liu, Y., Wang, L., & Yan, Y. (2023, October). Persistence Landscape-based Topological Data Analysis for Personalized Arrhythmia Classification. In 2023 IEEE 19th International Conference on Body Sensor Networks (BSN) (pp. 1-6). IEEE.

Ling, T., Zhu, Z., Zhang, Y., & Jiang, F. (2022). Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal. Applied Sciences, 12(20), 10370.

and many others studies should be analysed, compared against your work .

2) Line 23 : pls use the same number of digits.

3) If your focus is on Arrythmia, you sd give the prevalence of Arrythmia

4) there is a typo : "off of"

5) The difference between homology and homotopy is not clear

6) Some data is discarded: in relation to the real tiem series what does it mean ? Large Amplitude? High/Low Frequency ?

7) One page to describe well known evaluation metrics is too much, can you condense pag 10

8) Why do you think Gradient Boosted Decision Tree results as optimal ? A nd what about the other results ? Results are just reported and not commented at all

9) The standard deviation of the metrics across the 5 folds could be reported in the tables for completeness. it would be useful to highlight best results in bold in the tables

10) Fig. 6. Receiver Operator Characteristic Curve for Classification of Arrhythmia vs. Sinus Rhythm, presents very bad results, what is the reason of presenting it ?

11) A Table to compare your results with other studies that approach ECG rhythm classification through TDA and machine learning, would be recommended.

12) page 16 line 11 , there is a typo "nonempty"

In general the application of Topological Data Analysis in Cardiovascular Signals as in all Time Series, is interesting, could you give an easier explanation of what is the meaning of this approach based on shape in ECG ? Graphs/Images could be helpfuls

**Nanopublication comments: **

**Further comments: **

## 1 Comment

## meta-review by editor

Submitted by Tobias Kuhn on

The reviewers are in agreement that the work represents a technically sound contribution whose novelty lies in the the introduction of topological features in representing ECG signals for use in classification of ECG rhythms. However they also identify a few areas that require improvement for publication, specifically:

Karin Verspoor (https://orcid.org/0000-0002-8661-1544)