<p><span lang="EN-US">This work presents a unique detection approach for classifying epilepsy using the CHB_MIT dataset. The suggested system utilizes the discrete wavelet transform (DWT) technique, genetic algorithm (GA), and decision tree (DT). This model consists of three distinct steps. In the first one, we present a feature extraction method that uses a DWT of four levels on electroencephalogram (EEG) and electrocardiogram (ECG) signals. The second step is the process of feature selection, which entails the elimination of irrelevant features in order to produce datasets of superior quality. This is achieved via the use of correlation and GA techniques. The reduction in dimensionality of the dataset serves to decrease the complexity of the training process and effectively addresses the problem of overfitting. The third step utilizes a DT algorithm to make predictions based on the data of epileptic patients. The performance evaluation layer encompasses the implementation of our prediction model on the CHB-MIT dataset. The results achieved from this implementation show that using feature selection techniques and an ECG signal as additional information increases the detection model's performance. The averaging accuracy is 98.3%, the sensitivity is 96%, and the specificity is 99%.</span></p>
Wireless Power Transfer (WPT) with inductive coupling is the one most advanced techniques for powering biomedical implants, in recent decades has been the transmission of energy without the need of cables. The importants elements (indicators), power transfer efficiency (PTE) and power delivered to load (PDL), of a wireless power transfer systems . These keys are dependent on several design parameters of WPT system, such as the geometrical parameters of the coils, the separation of the sender (TX) and the receiver (RX). And also the operating frequency. The invention, design, and optimization of coils square spirals in a wireless energy transfer system using a resonant inductive link are the emphasis of this paper. Metaheuristic algorithms are among the optimization techniques used.
Abstract Research has been conducted to support an automatic diagnosis system that will relieve clinicians of their weary work by detecting epileptic seizures. In this paper, we suggest a novel method to automatically identify epilepsy crises based on electroencephalogram (EEG) and electrocardiogram (ECG) signals. The work to detect epileptic seizures from EEG and ECG signals is carried out in three stages. In the first stage, simultaneous EEG and ECG recordings captured from 24 channels are segmented into 10-second periods (where 23 are the EEG signals and one is the ECG signal). In the second stage, the extraction of the parameters of each channel from the time domain and, finally, the classification of the EEG and ECG signals into epileptic seizure and normal have been done using ANN. Experiment analysis shows that using the ECG signal as extra information has a high capacity for classification.
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