Comparative analysis of ECG signals under health problems and emotional states
Abstract
The state of such a complex system as the cardiovascular one can be reflected by the electrocardiogram (ECG). The study of this system has been of scientific interest because it has represented a challenge in the development of methods and application of tools in order to classify, detect and analyze the behavior of healthy hearts, hearts that present some functional deterioration of hearts under different conditions. Cardiac diseases are in the first place among various types of threats to life, due to their high incidence and mortality, and that is why interest has been unleashed in knowing the causes and in this way being able to face them. On the other hand, it has also been relevant to study the hearts of individuals who witnessed certain levels of anxiety, worry or fear, because these can lead to underlying diseases when feelings become excessive. That is why the purpose of this work is to present various investigations that address the study of the system in question through innovative methodologies and under the previously mentioned cardiac conditions.Downloads
References
Acharya, U. R., Sudarshan, V. K., Koh, J. E., Martis, R. J., Tan, J. H., Oh, S. L., Muhammad, A., Hagiwara, Y., Mookiah, M. R. K., Chua, K. P., et al. (2017). Application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals. Biomedical Signal Processing and Control, 31:31–43.
Butcher, J. C. (2016). Numerical methods for ordinary differential equations. John Wiley & Sons.
Ferreira, B. B., Savi, M. A., and de Paula, A. S. (2014). Chaos control applied to cardiac rhythms represented by ecg signals. Physica Scripta, 89(10):105203.
Golany, T., Freedman, D., and Radinsky, K. (2021). Ecg ode-gan: Learning ordinary differential equations of ecg dynamics via generative adversarial learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 134–141.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
Hall, J. E. (2011). Guyton y Hall. Tratado de fisiolog´ıa m´edica. Elsevier Health Sciences.
Jones, S. A. (2021). ECG notes: Interpretation and management guide. FA Davis.
Lin, Y.-Z. and Yu, S. N. (2018). Bispectrum and histogram features for the identification of atrial fibrillation based on electrocardiogram. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 5994–5997. IEEE.
Liu, S., Shao, J., Kong, T., and Malekian, R. (2020). Ecg arrhythmia classification using high order spectrum and 2d graph fourier transform. Applied Sciences, 10(14):4741.
Runge, C. (1895). Über die numerische auflösung von differentialgleichungen. Mathematische Annalen, 46(2):167–178.
Shuman, D. I., Ricaud, B., and Vandergheynst, P. (2016). Vertex-frequency analysis on graphs. Applied and Computational Harmonic Analysis, 40(2):260–291.
S¨ornmo, L. and Laguna, P. (2005). Bioelectrical signal processing in cardiac and neurological applications, volume 8. Academic Press.
Templos-Hern´andez, D. J., Quezada-T´ellez, L. A., Gonz´alez-Hern´andez, B. M., Rojas-Vite, G., Pineda-S´anchez, J. E., Fern´andez-Anaya, G., and Rodriguez- Torres, E. E. (2021). A fractional-order approach to cardiac rhythm analysis. Chaos, Solitons & Fractals, 147:110942.
Yazawa, T. (2015a). Modified Detrended Fluctuation Analysis (mDFA). American Society of Mechanical Engineers.
Yazawa, T. (2015b). Quantifying stress in crabs and humans using modified dfa. Advances in Bioengineering. Rijeka, Croatia–European Union: Intech, pages 359–382.
Yazawa, T. (2017). Anxiety, worry and fear: Quantifying the mind using ekg time series analysis. In Time Series Analysis and Applications. IntechOpen.