Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients’ health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.
Diversos estudios han demostrado que la sensibilidad a la insulina presenta fluctuaciones durante la fase lútea y folicular del ciclo menstrual que pueden generar alteraciones en el comportamiento del nivel de glucosa. Diversos estudios han demostrado que la sensibilidad a la insulina presenta fluctuaciones durante la fase lútea y folicular del ciclo menstrual que pueden generar alteraciones en el comportamiento del nivel de glucosa. Conocer la relación entre las fases del ciclo menstrual y la resistencia a la insulina es un objetivo de salud personalizada, enfocada a la mejora de la calidad de vida. En esta investigación se propone una modificación al modelo matemático desarrollado por Dalla Man et al. (Dalla Man, C., Rizza, R. A., & Cobelli, C., 2007) para incluir las diferentes etapas del ciclo menstrual sobre el comportamiento del nivel de glucosa en la sangre, teniendo en cuenta cambios en la sensibilidad a la insulina y el valor basal de glucosa en la sangre. Para comprobar el comportamiento del nivel de glucosa durante el ciclo menstrual descrito en la literatura se realizó una prueba de tolerancia a la glucosa en una individua sana regularmente menstruante y se comparó con resultados de simulación in-vitro. Los resultados permiten observar comportamiento descrito en el modelo propuesto al relacionar las diferentes fases del ciclo menstrual y la resistencia a la insulina.
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