## 5 Conclusions  
The ISV is an important constraint that induces difficulties when analyzing physiological data. This study focused on the influence of the ISV in the MS for SVM and compared several well-known selection criteria to identify a selection criterion suitable for physiological data with ISV. Seven selection criteria were simulated for nine classification problems. The results showed that LSSC was the most effective for the classification problems and that the effect was greater according to the degree of ISV in the data. Therefore, the LSSC  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **KSC** & **KSC2** & **DBTC** & **ESDR** & **XAB** & **GACV** & **LSSC** \\ \hline
[26] & 1 & - & - & - & 2 & 3 & - \\
[27] & 1 & - & 2 & - & 4 & 3 & - \\ This paper & 4 & 6 & 2 & 5 & 7 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Relative ranking of selection criteria in each study.  
could be tested preferentially for MS related to physiological data with large ISV. MS is one of the factors that determines the performance of an SVM classifier, and the aforementioned comparison results are expected to be helpful for the construction of a better SVM classifier for classification problems based on physiological data. The advantage of the proposed approach can be utilized in various physiological-data-based applications, including healthcare, human-computer interactions, and medical decision systems [44,45]. In the future, we plan to conduct more rigorous verification of these results using large-sized datasets. In addition, the design of a new selection criterion for physiological data is one of our future research objectives.  
Conceptualization, M.C. and J.J.J.; methodology, M.C. and J.J.J.; software, M.C.; validation, M.C. and J.J.J.; writing--original draft preparation, M.C.; writing--review and editing, M.C. and J.J.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This study constructed nine problems for simulations. Among them, data for problems 1, 2, and 6-9 are available online as follows : [1: [[https://physionet.org/content/drivedb/1.0.0/](https://physionet.org/content/drivedb/1.0.0/)]([https://physionet.org/content/drivedb/1.0.0/](https://physionet.org/content/drivedb/1.0.0/))] (accessed on 3 December 2021), [2: [[https://www.kaggle.com/qiirro/svell-heart-rate-variability-hvr](https://www.kaggle.com/qiirro/svell-heart-rate-variability-hvr)]([https://www.kaggle.com/qiirro/svell-heart-rate-variability-hvr](https://www.kaggle.com/qiirro/svell-heart-rate-variability-hvr))] (accessed on 3 December 2021), [6: [[http://bbci.de/competition/iv/](http://bbci.de/competition/iv/)]([http://bbci.de/competition/iv/](http://bbci.de/competition/iv/))] (accessed on 3 December 2021), [7-8: [[https://www.kaggle.com/qiirro/comford](https://www.kaggle.com/qiirro/comford)]([https://www.kaggle.com/qiirro/comford](https://www.kaggle.com/qiirro/comford))] (accessed on 3 December 2021), [9: [[https://www.kaggle.com/mondejar/mitbi-database](https://www.kaggle.com/mondejar/mitbi-database)]([https://www.kaggle.com/mondejar/mitbi-database](https://www.kaggle.com/mondejar/mitbi-database))] (accessed on 3 December 2021).  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1G1A1003578).  
The authors declare no conflict of interest.