Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Sistemas de cálculo, análise e operação virtual: interferências de campos elétricos e magnéticos produzidas por estações radio base de acesso múltiplo por divisão de código em banda larga - WCDMA

In this work it was developed mathematical resolutions taking as parameter maximum intensity values for the interference analysis of electric and magnetic fields and was given two virtual computer system that supports families of CDMA and WCDMA technologies. The first family were developed computational resources to solve electric and magnetic field calculations and power densities in Radio Base stations , with the use of CDMA technology in the 800 MHz band , taking into account the permissible values referenced by the Commission International Protection on non-Ionizing Radiation . The first family is divided into two segments of calculation carried out in virtual operation. In the first segment to compute the interference field radiated by the base station with input information such as radio channel power; Gain antenna; Radio channel number; Operating frequency; Losses in the cable; Attenuation of direction; Minimum Distance; Reflections. Said computing system allows to quickly and without the need of implementing instruments for measurements, meet the following calculated values: Effective Radiated Power; Sector Power Density; Electric field in the sector; Magnetic field in the sector; Magnetic flux density; point of maximum permissible exposure of electric field and power density. The results are shown in charts for clarity of view of power density in the industry, as well as the coverage area definition. The computer module also includes folders specifications antennas, cables and towers used in cellular telephony, the following manufacturers: RFS World, Andrew, Karthein and BRASILSAT. Many are presented "links" network access "Internet" to supplement the cable specifications, antennas, etc. . In the second segment of the first family work with more variables , seeking to perform calculations quickly and safely assisting in obtaining results of radio signal loss produced by ERB . This module displays screens representing propagation systems denominated "A" and "B". By propagating "A" are obtained radio signal attenuation calculations in areas of urban models , dense urban , suburban , and rural open . In reflection calculations are present the reflection coefficients , the standing wave ratio , return loss , the reflected power ratio , as well as the loss of the signal by mismatch impedance. With the spread " B" seek radio signal losses in the survey line and not targeted , the effective area , the power density , the received power , the coverage radius , the conversion levels and the gain conversion systems radiant . The second family of virtual computing system consists of 7 modules of which 5 are geared towards the design of WCDMA and 2 technology for calculation of telephone traffic serving CDMA and WCDMA . It includes a portfolio of radiant systems used on the site. In the virtual operation of the module 1 is compute-: distance frequency reuse, channel capacity with noise and without noise, Doppler frequency, modulation rate and channel efficiency; Module 2 includes computes the cell area, thermal noise, noise power (dB), noise figure, signal to noise ratio, bit of power (dBm); with the module 3 reaches the calculation: breakpoint, processing gain (dB) loss in the space of BTS, noise power (w), chip period and frequency reuse factor. Module 4 scales effective radiated power, sectorization gain, voice activity and load effect. The module 5 performs the calculation processing gain (Hz / bps) bit time, bit energy (Ws). Module 6 deals with the telephone traffic and scales 1: traffic volume, occupancy intensity, average time of occupancy, traffic intensity, calls completed, congestion. Module 7 deals with two telephone traffic and allows calculating call completion and not completed in HMM. Tests were performed on the mobile network performance field for the calculation of data relating to: CINP , CPI , RSRP , RSRQ , EARFCN , Drop Call , Block Call , Pilot , Data Bler , RSCP , Short Call, Long Call and Data Call ; ECIO - Short Call and Long Call , Data Call Troughput . As survey were conducted surveys of electric and magnetic field in an ERB , trying to observe the degree of exposure to non-ionizing radiation they are exposed to the general public and occupational element. The results were compared to permissible values for health endorsed by the ICNIRP and the CENELEC .