Herramientas Fractales para el estudio de la Aracnofobia
DOI:
https://doi.org/10.29057/icbi.v13iEspecial.13783Palabras clave:
Fractalidad, estrés, fobias, variabilidad cardiaca, aracnofobiaResumen
El análisis de datos biológicos complejos es fundamental en la medicina y biofísica, ya que permite identificar patrones asociados a diversas condiciones de salud. En particular, el uso de herramientas avanzadas, como el análisis fractal, en el estudio de series temporales fisiológicas ha sido especialmente valioso para evaluar respuestas al estrés. Este enfoque ha permitido una comprensión más profunda de la variabilidad cardíaca en personas con fobias, como la aracnofobia. En este estudio se ha encontrado que técnicas como la biorretroalimentación de la variabilidad de la frecuencia cardíaca (HRV) son efectivas para reducir el miedo, ofreciendo nuevas oportunidades tanto para la investigación biofísica como para el diagnóstico temprano.
Descargas
Información de Publicación
Perfiles de revisores N/D
Declaraciones del autor
Indexado en
- Editor y comité editorial
- perfiles
- Sociedad académica
- N/D
Para obtener más información sobre un dato, haga clic 
PF es mantenido por el Public Knowledge Project
Citas
Abagaro, A. M., Barki, H., Ayana, G., Dawud, A. A., Thamineni, B. L., Jemal, T., y Choe, S.-w. (2024). Automated ecg signals analysis for cardiac abnormality detection and classification. Journal of Electrical Engineering & Technology, pp. 1–17.
Berntson, Gary G and Thomas Bigger Jr, J and Eckberg, Dwain L and Grossman, Paul and Kaufmann, Peter G and Malik, Marek and Nagaraja, Haikady N and Porges, Stephen W and Saul, J Philip and Stone, Peter H and others (1997). Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology, 34(6):623–648.
Birnbaum, Y., Rankinen, J., Jneid, H., Atar, D., y Nikus, K. (2022). The role of ecg in the diagnosis and risk stratification of acute coronary syndromes: an old but indispensable tool. Current cardiology reports, 24(2):109 118.
Cohen, J. (2016). A power primer.
Dong, X. (2005). Long workhours, work scheduling and work-related injuries among construction workers in the united states. Scandinavian journal of work, environment & health, pp. 329–335.
Electrophysiology, T. F. o. t. E. S. o. C. t. N. A. S. o. P. (1996). Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5):1043–1065.
Glass, L. y Mackey, M. C. (1988). From clocks to chaos: The rhythms of life. Princeton University Press.
Goessl, V. C., Curtiss, J. E., y Hofmann, S. G. (2017). The effect of heart rate variability biofeedback training on stress and anxiety: a meta-analysis. Psychological medicine, 47(15):2578–2586.
Goldberger, A. L., Amaral, L. A., Hausdorff, J. M., Ivanov, P. C., Peng, C.-K., y Stanley, H. E. (2002). Fractal dynamics in physiology: alterations with disease and aging. Proceedings of the national academy of sciences, 99(suppl 1):2466–2472.
González, P. (2012). Las matemáticas y la naturaleza. http://revistacienoliletras.blogspot.com/2012/05/las-matematicas-y-la-naturaleza.html. Accedido: [20/09/2024].
Ihmig, F. R., Gogeascoechea, A., Schafer, S., Lass-Hennemann, J., y Michael, T. (2020). Electrocardiogram, skin conductance and respiration from spiderfearful individuals watching spider video clips. Kantelhardt, J. (2002). Multifractal deterended fluctuation analysis of nonstationary time series. Physica, 316:81–91.
Kantelhardt, J. (2002). Multifractal deterended fluctuation analysis of nonstationary time series. Physica, 316:81–91.
Kim, H.-G., Cheon, E.-J., Bai, D.-S., Lee, Y. H., y Koo, B.-H. (2018). Stress and heart rate variability: a meta-analysis and review of the literature. Psychiatry investigation, 15(3):235.
Kiviniemi, A. M., Hautala, A. J., Kinnunen, H., Nissila, J., Virtanen, P., Karjalainen, J., y Tulppo, M. P. (2010). Daily exercise prescription on the basis of hr variability among men and women. Medicine & Science in Sports & Exercise, 42(7):1355–1363.
Lee, S.-R., Choi, E.-K., Han, K.-D., Jung, J.-H., Oh, S., y Lip, G. Y. (2018). Edoxaban in asian patients with atrial fibrillation: effectiveness and safety. Journal of the American College of Cardiology, 72(8):838–853.
Legaz-Arrese, A., López-Laval, I., George, K., Puente-Lanzarote, J. J., Moliner-Urdiales, D., Ayala-Tajuelo, V. J., Mayolas-Pi, C., y Reverter-Masia, J. (2015). Individual variability in cardiac biomarker release after 30 min of high-intensity rowing in elite and amateur athletes. Applied Physiology, Nutrition, and Metabolism, 40(9):951–958.
Lehrer, P., Kaur, K., Sharma, A., Shah, K., Huseby, R., Bhavsar, J., y Zhang, Y. (2020). Heart rate variability biofeedback improves emotional and physical health and performance: A systematic review and meta analysis. Applied psychophysiology and biofeedback, 45:109–129.
Lehrer, P. M. y Gevirtz, R. (2014). Heart rate variability biofeedback: how and why does it work? Frontiers in psychology, 5:756.
Mandelbrot, B. B. (1982). Thefractalgeometryofnature. WH Freedman and Co., New York, 1(983):1.
Mather, M. y Thayer, J. F. (2018). How heart rate variability affects emotion regulation brain networks. Current opinion in behavioral sciences, 19:98–104.
Moraes, ´I. A., Silva, T. D., Massetti, T., Menezes, L. D., Ribeiro, V. F., Tropiano, L. M., Barnab´e, V., Hoshi, R. A., Monteiro, C. B., y Fernandes, M. (2019). Fractal correlations and linear analyses of heart rate variability in healthy young people with different levels of physical activity. Cardiology in the Young, 29(10):1236–1242.
Muñoz, V. G. (2018). El color del ruido en el sueño de adultos mayores con y sin deterioro cognitivo. Tesis de máster, Universidad Autónoma del Estado de Hidalgo, Instituto de Ciencias Básicas e Ingeniería, Centro de Investigación en Matemáticas, Mineral de la Reforma, Hidalgo. Tesis para obtener el título de Licenciada en Matemáticas Aplicadas.
Peitgen, H.-O., Jurgens, H., Saupe, D., y Feigenbaum, M. J. (2004). Chaos and fractals: new frontiers of science, volumen 106. Springer.
Peng, C.-K., Havlin, S., Stanley, H. E., y Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: an interdisciplinary journal of nonlinear science, 5(1):82–87.
Rogers, B. y Gronwald, T. (2022). Fractal correlation properties of heart rate variability as a biomarker for intensity distribution and training prescription in endurance exercise: An update. Frontiers in Physiology, 13.
Shaffer, F. y Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in public health, 5:258.
Shaffer, F., McCraty, R., y Zerr, C. L. (2014). A healthy heart is not a metronome: an integrative review of the heart’s anatomy and heart rate variability. Frontiers in psychology, 5:1040.
Tanabe, N., Sato, S., Suki, B., y Hirai, T. (2020). Fractal analysis of lung structure in chronic obstructive pulmonary disease. Frontiers in physiology, 11:603197
Thayer, J. F. y Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of affective disorders, 61(3):201–216.
Therapeutics, L. (n.d.). The science of hrv biofeedback: A comprehensive guide. https://blog.getlief.com/the-science-of-hrv-biofeedback-a-comprehensive-guide/. Accessed: September 17, 2024.
Van Rossum, G. y Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace, Scotts Valley, CA.
Zick, S. M., Vautaw, B. M., Gillespie, B., y Aaronson, K. D. (2009). Hawthorn extract randomized blinded chronic heart failure (herb chf) trial. European journal of heart failure, 11(10):990–999.
Descargas
Publicado
Cómo citar
Número
Sección
