Cardiac dynamics predictions based on probability and entropy proportions of attractors: simulations normal - chronic and chronic - acute evolution

Predicciones de dinámica cardiaca basadas en probabilidad y proporciones de entropía de atractores: simulaciones de evolución normal-crónica y crónico-aguda

Published 2016-12-31


https://doi.org/10.30554/archmed.16.2.1730.2016
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Vol. 16 No. 2
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Rodríguez Velásquez, J. O., Prieto, S., & Ramirez, L. (2016). Cardiac dynamics predictions based on probability and entropy proportions of attractors: simulations normal - chronic and chronic - acute evolution. Archivos De Medicina (Manizales), 16(2), 223-236. https://doi.org/10.30554/archmed.16.2.1730.2016
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https://doi.org/10.30554/archmed.16.2.1730.2016
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Rodríguez Velásquez, J. O., Prieto, S., & Ramirez, L. (2016). Cardiac dynamics predictions based on probability and entropy proportions of attractors: simulations normal - chronic and chronic - acute evolution. Archivos De Medicina (Manizales), 16(2), 223-236. https://doi.org/10.30554/archmed.16.2.1730.2016

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Javier Oswado Rodríguez Velásquez
Signe Prieto
Leonardo Ramirez
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Objective: To develop physical and mathematical simulations of cardiac dynamics from normal states to acute conditions. Methods: 4 normal cardiac dynamics were taken, from which 4 simulations were made for each dynamic, starting from maximum and minimum values of heart rates and number of beats per hour. These values were used to make a simulation of the entire sequency through a random algorithm; after that, it was possible to build attractors. Subsequently, probabilities, entropies and entropy proportions were calculated, and each simulation diagnoses were established, determining paths for normal, chronic disease and acute illness. Results: The methodology allowed to establish measures of probability, entropy and proportions of the entropy of the attractor that quantify mathematically the level of severity of each simulation, showing variations between different levels of chronic and acute disease.Conclusion: The developed paths show the diagnostic capacity of this methodology to quantify variations in time and show slight alterations that can lead to acute conditions, being useful in the clinical setting.

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