Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems

Color Stability and Surface Properties of PMMA/ZrO2 Nanocomposite Denture Base Material after Using Denture Cleanser

Statement of Problem. Novel polymethyl methacrylate (PMMA) containing zirconium dioxide nanoparticles (nano-ZrO2) was suggested as a denture base material but there is a lack of information regarding denture cleanser effects. Objectives. This study aimed to evaluate denture cleanser effects on color stability, surface roughness, and hardness of PMMA denture base resin reinforced with nano-ZrO2. Materials and Methods. A total of 420 specimens were fabricated of unreinforced and nano-ZrO2 reinforced acrylic resin at 2.5% and 5%, resulting in 3 main groups. These groups were further subdivided (n = 10) according to immersion solution (distilled water, Corega, sodium hypochlorite, and Renew) and immersion duration. Surface roughness, hardness, and color were measured at baseline (2 days-T0) in distilled water and then after 180 and 365 days of immersion (T1 & T2) in water or denture cleansing solutions. Data was collected and analyzed using two-way ANOVA followed by Bonferroni post hoc test (α = 0.05). Results. Surface roughness increased significantly after denture cleanser immersion of unmodified and nano-ZrO2-modified PMMA materials while hardness decreased (P<0.001). The denture cleansers significantly affected the color of both PMMA denture bases (P<0.001). The immersion time in denture cleansers significantly affected all tested properties (P<0.001). Within denture cleansers, NaOCl showed the highest adverse effects (P<0.05) while Renew showed the least adverse effects. Conclusion. Denture cleansers can significantly result in color change and alter the surface roughness and hardness of denture base resin even with ZrO2 nanoparticles addition. Therefore, they should be carefully used