Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master–slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system’s effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data.

En este trabajo de investigación, se presenta el desarrollo de una interfaz gráfica en Python para la simulación del modelo mínimo de Bergman, el cual describe la interacción entre la glucosa y la insulina en pacientes con diabetes tipo 1. El modelo fue implementado en una interfaz que permite la simulación de diferentes escenarios de la enfermedad, facilitando el análisis de cómo diversos parámetros influyen en las dinámicas glucosa-insulina. La validación del modelo se llevó a cabo mediante comparaciones con estudios previos, confirmando su precisión en la representación de estas interacciones. Como resultado, se logró crear una herramienta que facilita la exploración y el estudio de las dinámicas de glucosa e insulina en diferentes condiciones, proporcionando una base para futuras investigaciones en el control de la glucosa en diabetes tipo 1.