Investigating Heart Rate Variability Index Classification in Macaca fascicularis and Humans: Exploring Applications for Personal Identification and Anonymization Studies
Volume 9, Issue 1, Page No 143-148, 2024
Author’s Name: Daisuke Hirahara1, Itaru Kanekoa),2, Junji Nishino3, Junichiro Hayano4, Oscar Martinez Mozos5, Emi Yuda2
View Affiliations
1Department of AI Research Lab, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, Kagoshima 891-0113, Japan
2Tohoku University, GSIS, Sendai, 980-8579, Japan
3The University of Electro-Communication, Tokyo, 182-8585, Japan
4 Nagoya City University, Nagoya, 467-8601, Japan
5Örebro University, Institutionen för Naturvetenskap och Teknik, Örebro, 701 82, Sweden
a)whom correspondence should be addressed. E-mail: kyhsubmit@it-aru.com
Adv. Sci. Technol. Eng. Syst. J. 9(1), 143-148 (2024); 
DOI: 10.25046/aj090114
Keywords: Macaca fascicularis, Privacy risks, Heart rate patters
Export Citations
In this paper, we determine the feasibility of differentiating between the heart rate patterns of Macaca fascicularis and human infants by comparing pertinent hyperparameters. This verification process was undertaken to ascertain the suitability of Macaca fascicularis heart rate data as a testbed for evaluating heart rate parameter privacy safeguarding methodologies. The biological characteristics of Macaca fascicularis bear significant resemblance to those of humans, which consequently renders them useful subjects in medical experiments alongside other laboratory animals. The process of capturing heartbeat data from Macaca fascicularis is notably akin to the methodologies used to record human cardiac activity. In other hand, the recent years have witnessed the construction of extensive heart rate databases, thus raising important considerations surrounding privacy in their usage. Heartbeat recordings, indeed, can provide a wealth of diverse information, necessitating careful handling to maintain data privacy. Specifically, a Holter monitor, a type of electrocardiogram device, can record cardiac electrical activity for over 24 hours. The statistical indices derived from these recordings prove useful for various types of analysis, and simultaneously hold information relating to individual behaviors and health conditions. The extent to which individuals can be identified within such expansive databases is a topic warranting exploration; however, few individuals have granted consent for their data to be used for such research purposes. Given this scenario, since the protection of personal data is not a requisite for Macaca fascicularis, the proposition of employing Macaca fascicularis data to investigate the potential for individual identification appears to be a plausible approach. The experiment verified the similarity of cynomolgus monkey heart rate data to human heart rate data. The results are similar, suggesting that it is appropriate to use cynomolgus monkey heart rate data for personality identification experiments.
Received: 09 November 2023, Revised: 25 January 2024, Accepted: 26 January 2024, Published Online: 22 February 2024
- B. Koo, D. Lee1, P. Kang, K. Jeong, S. Lee, K. Kim, Y. Lee, J. Huh, Y. Kim, S. Park, Y.B. Jin, S. Kim, J. Kim, Y. Son and S. Lee, “Reference values of hematological and biochemical parameters in young-adult cynomolgus monkey (Macaca fascicularis) and rhesus monkey (Macaca mulatta) anesthetized with ketamine hydrochloride,” PMID: 32257895 PMCID: PMC7081622, 2019.
- Y. Uno, S. Uehara, H. Yamazaki, “Genetic polymorphisms of drug-metabolizing cytochrome P450 enzymes in cynomolgus and rhesus monkeys and common marmosets in preclinical studies for humans,” PMID: 29277691, DOI: 10.1186/s42826-019-0006-0.
- Y. Noguchi, I. Tawara, K. Kondo, H. Nigi, T. Tanaka, “Electrocardiographic studies in the Japanese monkey (Macaca fuscata) with special reference to the effect of anesthesia with barbiturates,” Primates 10, 273–283, 1969, DOI: 10.1007/BF01730348.
- S. Aziz, S. Ahmed, M. Alouini, “ECG-based machine-learning algorithms for heartbeat classification,” Sci Rep 11, 18738 (2021), DOI: 10.1038/s41598-021-97118-5.
- A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. Peng, H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation 101(23):e215-e220, 2000 (June 13), doi: 10.1161/01.CIR.101.23.e215.
- E. Yuda, Y. Furukawa, Y.a Yoshida, J. Hayano & ALLSTAR Research Group, “Association Between Regional Difference in Heart Rate Variability and Inter-prefecture Ranking of Healthy Life Expectancy: ALLSTAR Big Data Project in Japan,” Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 194), 2017.
- E. Yuda, M. Kisohara, Y. Yoshida & J. Hayano, “Constituent factors of heart rate variability ALLSTAR big data analysis,” Wireless Networks, 28(3):1287–1292, 2022, DOI: 10.1007/s11276-018-01898-0.
- T. Yukishita, K. Lee, S. Kim,Y. Yumoto, A. Kobayashi, T. Shirasawa, “Age and Sex-Dependent Alterations in Heart Rate Variability Profiling the Characteristics of Men and Women in Their 30s,” ANTI-AGING MEDICINE. 2010; 7: 94-99, 2010.
- R. K. Tripathy, A. Acharya, S. K. Choudhary, “Gender Classification from ECG Signal Analysis using Least Square Support Vector Machine,” American Journal of Signal Processing, 2(5): 145-149, 2012 doi: 10.5923/j.ajsp.20120205.08.
- I. Kaneko, J. Hayano, E. Yuda, “How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis,” BMC Research Notes 16, Article number: 5, 2023, doi:10.1186/s13104-022-06270-2.