Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.

This paper presents aspects concerning the bivariate analysis of the textiles surfaces treated by RF plasma technologies (Ar RF plasma and SF6 RF plasma) to obtain the hydrophobic effect. Besides this, it was studied the remnant or temporary hydrophobic effect obtained by polytetrafluoroethylene (PTFE) thin-film by physical vapor deposition (PVD) technique and SF6 RF plasma technique. In this paper, there are presented the bivariate analysis of the parameters and characterization of hydrophobic textile samples (cotton 100% with mass 401 g/m2) by investigation of the water contact angle using the device VCA OPTIMA and resistance to surface wetting by James Heal spray tester. The morphological modification of the textile was evaluated by scanning electron microscope with magnification x8000. Following these investigations there has been observed that the treatment with polytetrafluoroethylene is even more harmful to the environment, offering a hydrophobic effect resistant in time, for approximative 101 days, and the treatment by SF6 RF plasma confer a temporary effect for 24 hours.

This paper presents several aspects of the multivariate analysis of electroconductive composite based on Copper (Cu) and Graphene. The analysis was developed by using the parameters (dependent and independent variables), which characterize the composite materials with electroconductive properties. The experimental samples were obtained by using 100% cotton fabrics with different structures. The goals followed through the variation of the fabric structures (e.g., plain weave, twill, panama, ribs) were to investigate if the fabric structure or ratio has or not influence on electroconductive properties of the textile materials obtained by conductive coating. The samples created were based on standard, and 3D digital printing technologies, more specifically on the textile surface, have deposited conductive paste containing copper microparticles and graphene filaments. The initial coating with conductive polymeric paste based Cu was developed by scraping of the paste on the fabric. Previously the 3D printing advanced technology by fused deposition modeling (FDM) of the Conductive Graphene filaments was used

This paper presents a study concerning the preliminary treatments in radiofrequency (RF)oxygen (O₂) plasma used to obtain a hydrophilic effect on raw cotton fabrics followed by electroconductive thin film deposition to obtain electroconductive textile surfaces. In addition, this study presents a multivariate correlation analysis of experimental parameters. The treatment using RF plasma O₂ aimed to increase the hydrophilic character of the raw fabric and adherence of paste-based polymeric on polyvinyl alcohol (PVA) matrix and nickel (Ni), silver (Ag) or copper (Cu) microparticles. The purpose of the research was to develop electroconductive textiles for flexible electrodes, smart materials using a clean technology such as radiofrequency (RF) plasma O₂ to obtain a hydrophilic surface with zero wastewater and reduced chemicals and carbon footprint. To achieve the foreseen results, we used advanced functionalization technologies such as RF plasma O₂, followed by scraping a thin film of conductive paste-based Ni, Ag or Cu microparticles, and multivariate correlation methods to observe the dependence between parameters involved (dependent and independent variables). Overall, the fabrics treated in plasma with O₂ using a kHz or MHz generator and power 100–200 W present an excellent hydrophilic character obtained in 3 min. After RF O₂ plasma functionalization, a thin film based on polymeric matrix PVA and Ni microparticles have been deposited on the fabric surface to obtain electroconductive materials.