Temporal inequality of RR intervals like a new psychophysiological indicator of mental stress
DOI:
https://doi.org/10.56294/saludcyt2024654Keywords:
Heart Rate Variability, Mental Stress, Gini coefficient, Autonomic Nervous System, Mental Arithmetic TestAbstract
Introduction: Gini coefficient (Gini index or Gini ratio) is a parameter that is normally used in economy to measure the income distribution in a country or in the whole wide world, but it can be used to measure any kind of distribution. In the present study it is exposed an innovative proposal of application of the Gini coefficient to Heart Rate Variability (HRV) like a psychophysiological indicator of mental stress.
Objective: to assess the application of the Gini coefficient as a psychophysiological indicator of mental stress.
Methods: a non-observational crossover study, carried out in the biomedical laboratory of the Medical University of Santiago de Cuba. The involved participants are 13 healthy individuals (age 19 ± 1,5 years). Heart rate was continuously recorded at rest (5 minutes) and during a mental stress (5 minutes). Linear and nonlinear methods of heart rate variability were assessed, and 2 new indicators (Sequential and Non-Sequential Gini) were calculated and proposed to measure HRV differences between states.
Results: when comparing rest and mental stress conditions, a sensible decrease of the traditional indicators of the HRV was founded (p<0,05), an increase of the heart rate (p=0,004) and of the Sequential Gini (p=0,004) and Non-Sequential Gini (p=0,04).
Conclusions: the results suggest that temporary inequality of the RR intervals analyzed from the Gini coefficient could be an adequate indicator of sympathetic activity present during the mental stress
References
1. Zhang, H., Yao, Z., Lin, L., Sun, X., Shi, X., & Zhang, L. (2019). Early life stress predicts cortisol response to psychosocial stress in healthy young adults. PsyCh Journal, 8(3), 353-362. https://doi.org/10.1002/pchj.278
2. González-Velázquez, V. E., Pedraza-Rodríguez, E. M., Carrazana-Escalona, R., Moreno-Padilla, M., Muñoz-Bustos, G. A., & Sánchez-Hechavarría, M. E. (2020). Cardiac vagal imbalance to the isometric sustained weight test in adolescents with emotional eating behavior. Physiology & Behavior, 223, 112994. https://doi.org/10.1016/j.physbeh.2020.112994
3. Doom, J. R., Reid, B. M., Nagel, E., Gahagan, S., Demerath, E. W., & Lumeng, J. C. (2020). Integrating anthropometric and cardiometabolic health methods in stress, early experiences, and development (SEED) science. Developmental Psychobiology, n/a(n/a). https://doi.org/10.1002/dev.22032
4. Liu, J. J. W., Reed, M., & Vickers, K. (2019). Reframing the individual stress response: Balancing our knowledge of stress to improve responsivity to stressors. Stress and Health, 35(5), 607-616. https://doi.org/10.1002/smi.2893
5. Qi, M., & Gao, H. (2020). Acute psychological stress promotes general alertness and attentional control processes: An ERP study. Psychophysiology, 57(4), e13521. https://doi.org/10.1111/psyp.13521
6. Patel, N. M., Limberg, J. K., Covassin, N., Somers, V. K., Joyner, M. J., & Baker, S. E. (2020). Attenuated Sympathetic Baroreflex Sensitivity Evoked by Acute Mental Stress but not Prolonged Sleep Restriction in Healthy Adults. The FASEB Journal, 34(S1), 1-1. https://doi.org/10.1096/fasebj.2020.34.s1.02537
7. Sayed, K. E., Macefield, V. G., Hissen, S. L., Joyner, M. J., & Taylor, C. E. (2018). Blood pressure reactivity at onset of mental stress determines sympathetic vascular response in young adults. Physiological Reports, 6(24), e13944. https://doi.org/10.14814/phy2.13944
8. Li, H., Kilgallen, A. B., Münzel, T., Wolf, E., Lecour, S., Schulz, R., Daiber, A., & Laake, L. W. V. (2019). Influence of mental stress and environmental toxins on circadian clocks: Implications for redox regulation of the heart and cardioprotection. British Journal of Pharmacology, n/a(n/a). https://doi.org/10.1111/bph.14949
9. Pedraza-Rodríguez, E. M., Almira-Gómez, C. R., Reyna, S. C., Bueno-Revilla, D. de J., López-Galán, E., & Sánchez-Hechavarría, M. E. (2019). Modifications Of The Non-Linear Parameters Of The Heart Rate Variability Related To The Mental Arithmetic Test. Revista Cubana de Investigaciones Biomédicas, 38(1), Article 1. http://www.revibiomedica.sld.cu/index.php/ibi/article/view/161
10. Pedraza-Rodríguez, E. M., González-Velázquez, V. E., Montes de Oca-Carmenaty, M., González-Medina, K. N., Muñoz-Bustos, G. A., Bueno-Revilla, D. de J., López-Galán, E., & Sánchez-Hechavarría, M. E. (2020). Respuesta autonómica cardiovascular al estrés mental inducido por la prueba de cálculo aritmético. Revista Cubana de Investigaciones Biomédicas, 39(4), e625. http://www.revibiomedica.sld.cu/index.php/ibi/article/view/817
11. Mejía‐Mejía, E., Torres, R., & Restrepo, D. (2018). Physiological coherence in healthy volunteers during laboratory-induced stress and controlled breathing. Psychophysiology, 55(6), e13046. https://doi.org/10.1111/psyp.13046
12. Kühnel, A., Kroemer, N. B., Elbau, I. G., Czisch, M., Sämann, P. G., Walter, M., & Binder, E. B. (2020). Psychosocial stress reactivity habituates following acute physiological stress. Human Brain Mapping, 41(14), 4010-4023. https://doi.org/10.1002/hbm.25106
13. Lombardo, D. M., & Vick, R. S. (2019). Relationship Between Heart Rate Variability and Perceived Stress in Healthy College-Aged Adults. The FASEB Journal, 33(S1), 741.2-741.2. https://doi.org/10.1096/fasebj.2019.33.1_supplement.741.2
14. Electrophysiology Task Force of the European Society of Cardiology the North American Society of Pacing. (1996). Heart Rate Variability. Circulation, 93(5), 1043-1065. https://doi.org/10.1161/01.CIR.93.5.1043
15. Sassi, R., Cerutti, S., Lombardi, F., Malik, M., Huikuri, H. V., Peng, C.-K., Schmidt, G., Yamamoto, Y., Reviewers, D., Gorenek, B., Lip, G. Y. H., Grassi, G., Kudaiberdieva, G., Fisher, J. P., Zabel, M., & Macfadyen, R. (2015). Advances in heart rate variability signal analysis: Joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. EP Europace, 17(9), 1341-1353. https://doi.org/10.1093/europace/euv015
16. Bravi, A., Longtin, A., & Seely, A. J. (2011). Review and classification of variability analysis techniques with clinical applications. Biomed Eng Online, 10, 90. https://doi.org/10.1186/1475-925X-10-90
17. Scott, B. G., Alfano, C. A., Russell, J. D., & Weems, C. F. (2019). Heart rate variability and anxious arousal: Unique relations with sleep-related problems in stress-exposed adolescents. Developmental Psychobiology, 61(8), 1180-1190. https://doi.org/10.1002/dev.21883
18. Sánchez-Hechavarría, M. E., Ghiya, S., Carrazana-Escalona, R., Cortina-Reyna, S., Andreu-Heredia, A., Acosta-Batista, C., & Saá-Muñoz, N. A. (2019). Introduction of Application of Gini Coefficient to Heart Rate Variability Spectrum for Mental Stress Evaluation. Arquivos Brasileiros de Cardiologia, 113(4), 725-733. https://doi.org/10.5935/abc.20190185
19. You, K.-J., Noh, G.-J., & Shin, H.-C. (2016). Spectral Gini Index for Quantifying the Depth of Consciousness. Computational Intelligence and Neuroscience, 2016, 2304356. https://doi.org/10.1155/2016/2304356
20. Viehweger, A., Riffert, T., Dhital, B., Knösche, T. R., Anwander, A., Stepan, H., Sorge, I., & Hirsch, W. (2014). The Gini coefficient: A methodological pilot study to assess fetal brain development employing postmortem diffusion MRI. Pediatric Radiology, 44(10), 1290-1301. https://doi.org/10.1007/s00247-014-3002-4
21. Wright Muelas, M., Mughal, F., O’Hagan, S., Day, P. J., & Kell, D. B. (2019). The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data. Scientific Reports, 9(1), 17960. https://doi.org/10.1038/s41598-019-54288-7
22. Manikandan, M. S., & Soman, K. P. (2012). A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomedical Signal Processing and Control, 7(2), 118-128. https://doi.org/10.1016/j.bspc.2011.03.004
23. Ramshur, J. T. (2010). Design, Evaluation, and Application of Heart Rate Variability Analysis Software (HRVAS). University of Memphis. https://books.google.cl/books?id=vHZYAQAACAAJ
24. Mitov, I. P. (1998). A method for assessment and processing of biomedical signals containing trend and periodic components. Med Eng Phys, 20(9), 660-668.
25. Vuksanovic, V., & Gal, V. (2007). Heart rate variability in mental stress aloud. Med Eng Phys, 29(3), 344-349. https://doi.org/10.1016/j.medengphy.2006.05.011
26. Sloan, R. P., Korten, J. B., & Myers, M. M. (1991). Components of heart rate reactivity during mental arithmetic with and without speaking. Physiology & behavior, 50(5), 1039-1045.
27. Bernardi, L., Wdowczyk-Szulc, J., Valenti, C., Castoldi, S., Passino, C., Spadacini, G., & Sleight, P. (2000). Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability. J Am Coll Cardiol, 35(6), 1462-1469.
28. Hidaka, O., Yanagi, M., & Takada, K. (2004). Mental stress-induced physiological changes in the human masseter muscle. Journal of dental research, 83(3), 227-231.
29. Linden, W. (1991). What do arithmetic stress tests measure? Protocol variations and cardiovascular responses. Psychophysiology, 28(1), 91-102.
30. Ushiyama, K., Ogawa, T., Ishii, M., Ajisaka, R., Sugishita, Y., & Ito, I. (1991). Physiologic neuroendocrine arousal by mental arithmetic stress test in healthy subjects. American Journal of Cardiology, 67(1), 101-103.
31. Gastwirth, J. L. (1972). The estimation of the Lorenz curve and Gini index. The review of economics and statistics, 306-316.
32. Quintana, D. S. (2017). Statistical considerations for reporting and planning heart rate variability case-control studies. Psychophysiology, 54(3), 344-349. https://doi.org/10.1111/psyp.12798
33. Jarczewski, J., Furgała, A., Winiarska, A., Kaczmarczyk, M., & Poniatowski, A. (2019). Cardiovascular response to different types of acute stress stimulations. Folia Medica Cracoviensia, 59(4), 95-110. https://doi.org/10.24425/fmc.2019.131383
34. Pereira, T., Almeida, P. R., Cunha, J. P. S., & Aguiar, A. (2017). Heart rate variability metrics for fine-grained stress level assessment. Computer Methods and Programs in Biomedicine, 148, 71-80. https://doi.org/10.1016/j.cmpb.2017.06.018
35. Hur, C., Zhan, T., Thrift, A. P., Vaughan, T. L., & Feuer, E. J. (2019). Lorenz Curves and Gini Coefficient Analyses Indicate Inefficiencies in Esophageal Adenocarcinoma Screening. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 17(3), 560-562.e2. https://doi.org/10.1016/j.cgh.2018.05.002
36. Laborde, S., Mosley, E., & Thayer, J. F. (2017). Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting. Frontiers in Psychology, 08. https://doi.org/10.3389/fpsyg.2017.00213
37. Vanderlei, L. C. M., Pastre, C. M., Freitas Júnior, I. F., & Godoy, M. F. de. (2010). Índices geométricos de variabilidad de la frecuencia cardíaca en niños obesos y eutróficos. Arquivos Brasileiros de Cardiologia, 95(1), 35-40. https://doi.org/10.1590/S0066-782X2010005000082
38. Torres-Leyva, M., Carrazana-Escalona, R., Ormigó-Polo, L. E., Ricardo-Ferro, B. T., López-Galán, E., Ortiz-Alcolea, L., & Sánchez-Hechavarría, M. E. (2019). Cardiovascular autonomic response during the Cuban dynamic weight-bearing test. CorSalud, 11(1), 1-10. http://revcorsalud.sld.cu/index.php/cors/article/download/342/842
39. González-Velázquez, V. E., Pedraza-Rodríguez, E. M., Nápoles-Zaldívar, Y., Sánchez-Guerra, J. A., Muñoz-Bustos, G. A., Rodríguez-Nuviola, J., Bueno-Revilla, D. de J., López-Galán, E., & Sánchez-Hechavarría, M. E. (2020). Differences in linear Parameters of the basal autonomic balance between medical students and young baseball players. , 12(3), 301-311. http://revcorsalud.sld.cu/index.php/cors/article/download/452/1245
40. Billman, G. E., Huikuri, H. V., Sacha, J., & Trimmel, K. (2015). An introduction to heart rate variability: Methodological considerations and clinical applications. Front Physiol, 6, 55. https://doi.org/10.3389/fphys.2015.00055
41. Ristić-Djurović, J. L., Ćircović, S., Mladenović, P., Romcevic, N., & Trbovich, M. (2018). Analysis of methods commonly used in biomedicine for treatment versus control comparison of very small samples. Computer Methods and Programs in Biomedicine. 157:153-62. https://doi.org/10.1016/j.cmpb.2018.01.026
Published
Issue
Section
License
Copyright (c) 2024 Miguel Enrique Sanchez-Hechavarria, Ramon Carrazana-Escalona, Sergio Cortina-Reyna, Victor Ernesto González-Velázquez, Elys María Pedraza-Rodríguez, Adán Andreu-Heredia, Erislandis López-Galán (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
