Abstract
In this work, the optimization of a modular neural network for obtaining the trend of blood pressure is presented. Three modules are used, the first for obtaining the systolic pressure trend, the second one for the diastolic pressure trend and the last one for the heart rate trend. For each module of the neural network the layers and neurons are optimized, to find the architecture that will generate optimal results. In this case 47 readings of 50 patients are used to perform the optimization, these readings are obtained through a device called ambulatory blood pressure monitoring which is a non-invasive device that takes the patient’s reading every 20 min on the day and every 30 min at night, in twenty-four hours. Once the architectures for each module are obtained by Particle Swarm Optimization, we select the architecture that presented the minimal error and is tested with a set of 25 patients who were not trained by the neural network, to test whether the training was performed properly.
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Acknowledgements
The authors would like to express thank to the Consejo Nacional de Ciencia y Tecnologia and Tecnologico Nacional de Mexico/Tijuana Institute of Technology for the facilities and resources granted for the development of this research.
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Miramontes, I., Melin, P., Prado-Arechiga, G. (2020). Particle Swarm Optimization of Modular Neural Networks for Obtaining the Trend of Blood Pressure. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_19
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