The aim of this article is to design a new telehealth system with secured wireless transmission and classification of multiple biosignals using e‐Health sensors platform and Xbee modules with Arduino Uno and Raspberry Pi as acquisition and processing units, respectively. The collected data, such as temperature, airflow, position, Galvanic skin response and oxygen in the blood can be evaluated in order to monitor patient health state using threshold detection. The prediction of the cardiac state based on automatic identification of arrhythmias is validated by the classification of ElectroCardioGram (ECG) signals using Artificial Intelligence (AI) by exploiting TensorFlow and Keras tools. Different AI algorithms and a combination with different Machine Learning (ML) basing to transfer learning approach are tested. These algorithms include Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Support Vector Machine (SVM), K‐Nearest Neighbour (KNN) and Random Forest (RF). At first, ANN and CNN are used to classify ECG‐scalogram images using softmax, then the used CNN model (VGG16) is employed to extract features and pass them to other traditional classifiers (SVM, KNN and RF) allowing to evaluate and select the best classifier, such that the ECG signal can be classified into four categories namely Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Congestive Heart Failure (CHF) and other cardiac arrhythmia (ARR). The proposed method has been evaluated using real recorded signals and four PhysioNet databases. A Graphical User Interface (GUI) has been designed with C# under Visual Studio IDE allowing to display the results using personal computer (PC) or a network linked phone, which makes it possible to transfer the diagnosis with the prediction results to a remote clinic control room as Internet of Things (IoT) system application. The best classification accuracy of 99.56% is attained, confirming that the designed system allows a good trade‐off between low cost and performances in addition, it is easy to use with quick access to multiple biosignals. It has improved vital characteristics monitoring and diagnosis services quality under a robust secured wireless transmission using lightweight chaos‐based algorithm, thus preventing loss of life during critical health situations.
This paper presents a new way of designing a multi-wing chaotic system. The proposed design is based on 3D continuous chaotic system of Lorenz, improved by introducing a saw-tooth and sine functions. The basic proprieties of the proposed system are analyzed using of equilibrium points, phase portrait, Lyapunov exponent, and bifurcation diagram. Furthermore, the modeling of the design is based on Euler method using hardware description language (VHDL) and validated on Xilinx Virtex-II-Pro FPGA platform. Fixed-point arithmetic coding is employed to represented data on 32 bits (16Q16). Finally, the proposed system used to design a new chaos-based TRNG True Random Number Generators by analyzing its chaotic dynamical behavior and FPGA implementation performances. The proposed hardware architecture is based on two stages of pipeline and parallel structure (only 2 clock cycles). Experimental implementation results demonstrate that the design can achieve a maximum operating frequency of 12.649 MHz and a throughput of 202 Mbit/s. Besides, the random bit sequences produced by TRNG have been successfully passed the NIST-800-22 statistical standards tests. The proposed multi-wing attractor presents also complex dynamics and it can be applied in many engineering applications, especially in embedded cryptographic applications.
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