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Abstract Introduction Military police activity individuals performing operational activity remain 12 hours using mandatory safety equipment. This work aimed to verify the electromyographic response in operational military police officers before and after a cycle of two working days. Methods Forty-four male individuals were evaluated, with an average age of 34.59 ± 8.05. The used protocol consisted in the evaluation of paravertebral muscles and rectus abdominis muscles in a maximum isometric voluntary contraction test (MVC) during trunk extension movements, starting from the sitting position. Moreover, the Roland-Morris functional evaluation questionnaire and the Corlett and Manenica diagram for painful areas were used. An electromyograph with 16 pre-set channels was used. Signals were processed in time (EMGME) and spectral (EMGMF) domains, using the MatLab® program. The Shapiro-Wilk test and Wilcoxon Signed Ranks Test were applied. Statistical analyses were performed through the SPSS v21.0 software and Microsoft Office Excel 2010, considering p < 0.05 as significance level. Results Results showed statistical differences in the post-working day for time analysis, an EMGME decrease in the right rectus abdominis muscle (p = 0.016) and in the age-stratified sample, with individuals over 31 years old (p = 0.016); in the spectral analysis, EMGMF reduction in the right iliocostalis (p = 0.027) and right and left side in the stratified sample, in individuals over 31 years old and with more than 10 years of service. Conclusion The used protocol highlighted a decrease in the amplitude of the electromyographic signal, as well as possible muscle fatigue on the right side where officers usually carry their weapons.

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Abstract Introduction The interpretation of brain tumors and abscesses MR spectra is complex and subjective. In clinical practice, different experimental conditions such as field strength or echo time (TE) reveal different metabolite information. Our study aims to show in which scenarios magnetic resonance spectroscopy can differentiate among brain tumors, normal tissue and abscesses using classification algorithms. Methods Pairwise classification between abscesses, brain tumor classes, and healthy subjects tissue spectra was performed, also the multiclass classification between meningiomas, grade I-II-III gliomas, and glioblastomas and metastases, in 1.5T short TE (n = 195), 1.5T long TE (n = 231) and 3.0T long TE (n = 59) point resolved spectroscopy setups, using LCModel metabolite concentration as input to classifiers. Results Areas under the curve of the Receiver Operating Characteristic above 0.9 were obtained for the classification between abscesses and all classes except glioblastomas, reaching 0.947 when classifying against metastases, grade I-II gliomas and glioblastomas (0.980), meningiomas and glioblastomas (0.956), grade I-II gliomas and metastases (0.989), meningiomas and metastases (0.990), and between healthy tissue and all other classes in both conditions except for anaplastic astrocytomas in short TE 1.5T setup. When the multiclass classification agrees with radiological diagnosis the accuracy reaches 96.8% for short TE and 98.9% for long TE. Conclusions The results in the three conditions were similar, highlighting comparable quality, robust quantification and good regularization and flexibility in either algorithm. Multiclass classification provides useful information to the radiologist. These findings show the potential of the development of decision support systems as well as tools for the accompaniment of treatments.

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Abstract Introduction Early detection of epilepsy by the review of large electroencephalographic (EEG) recordings is very stressful, time-consuming, and subjective for neurologists. Several automatic seizure detection systems have been proposed in the literature to solve this problem. Methods This study proposes two complementary wavelet-based approaches for detecting epileptiform paroxysms in EEG signals. First methodology applied the wavelet multiresolution analysis (MRA) to filter non-epileptiform activity in long-term EEG. Second methodology used the wavelet dyadic scalogram to analyze which scales were related to the epileptiform paroxysms. For tests, 65 wavelet functions were selected between daubechies, biorthogonal, symlets, reverse biorthogonal and coiflet wavelet families in order to evaluate their performance. Results For MRA, it was noted a better performance by using the db4 function, by reaching 48.30% of energy with 8 wavelet coefficients, 0.717658 of correlation and 36.799 of root mean square error (RMSE). For wavelet dyadic scalograms, were chosen bior3.9 and rbio1.5 functions, by reaching 77.98% of sensitivity, 94.08% of specificity, 87.87% of efficiency and 0.9613 of area under the curve (AUC value) by using bior3.9. Conclusion The presented approaches are highly complementary for a whole automatic seizure detection system by using the MRA as pre-processing stage to filter non-epileptiform activity, and wavelet dyadic scalogram for extracting desired features from filtered EEG signals.

Resumo em Inglês:

Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.

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Abstract Introduction In the last 28 years, the scientific community has been using elastography to evaluate the mechanical properties of biological tissue. The aim of this work was the optimization of the UDmV method, presented in Part I of the series, by means of modifying the technique employed to generate the reference sine and cosine functions, used for phase-quadrature demodulation, and determining how this modification improved the performance of the method. Additionally, the UDmV was employed to characterize the acoustic and mechanical properties of a 7% gelatin phantom. Methods A focused transducer, TF, with a nominal frequency of 2.25 MHz, was used to induce the shear waves, with frequency of 97.644 Hz. Then, the modified UDmV method was used to extract the phase and quadrature components from ultrasonic RF echo-signals collected from four positions along the propagation path of the shear wave, which allowed the investigation of the medium vibration caused by wave propagation. The phase velocity, cs, and attenuation, αs, of the phantom were measured and employed in the calculation of shear modulus, μ, and viscosity, η. Results The computational simulation demonstrated that the modification in UDmV method resulted in more accurate and precise estimates of the initial phases of the reference sinusoidal functions used for phase-quadrature demodulation. The values for cs and μ of 1.31 ± 0.01 m·s-1 and 1.66 ± 0.01 kPa, respectively, are very close to the values found in the literature (1.32 m·s-1 and 1.61 kPa) for the same material. Conclusion The UDmV method allowed estimating of the acoustic and viscoelastic parameters of phantom.

Resumo em Inglês:

Abstract Introduction Historically, assessing the quality of human gait has been a difficult process. Advanced studies can be conducted using modern 3D systems. However, due to their high cost, usage of these 3D systems is still restricted to research environments. 2D systems offer simpler and more affordable solutions. Methods In this study, the gait of 40 volunteers walking on a treadmill was recorded in the sagittal plane, using a 2D motion capture system. The extracted joint angles data were used to create cyclograms. Sections of the cyclograms were used as inputs to artificial neural networks (ANNs), since they can represent the kinematic behavior of the lower body. This allowed for prediction of future states of the moving body. Results The results indicate that ANNs can predict the future states of the gait with high accuracy. Both single point and section predictions were successfully performed. Pearson’s correlation coefficient and matched-pairs t-test ensured that the results were statistically significant. Conclusion The combined use of ANNs and simple, accessible hardware is of great value in clinical practice. The use of cyclograms facilitates the analysis, as several gait characteristics can be easily recognized by their geometric shape. The predictive model presented in this paper facilitates generation of data that can be used in robotic locomotion therapy as a control signal or feedback element, aiding in the rehabilitation process of patients with motor dysfunction. The system proposes an interesting tool that can be explored to increase rehabilitation possibilities, providing better quality of life to patients.

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Abstract Introduction According to the World Health Organization, about 9.2% of the 28 million newborns worldwide are stillborn. Besides, about 358,000 women died due to complications related to pregnancy in 2015. Part of these deaths could have been avoided with improving prenatal care agility to recognize problems during pregnancy. Based on that, many efforts have been made to provide technologies that can contribute to offer better access to information and assist in decision-making. In this context, this work presents an architecture to automate the classification and referral process of pregnant women between the basic health units and the referral hospital through a Telehealth platform. Methods The Telehealth architecture was developed in three components: The data acquisition component, responsible for collecting and inserting data; the data processing component, which is the core of the architecture implemented using expert systems to classify gestational risk; and the post-processing component, in charge of the delivery and analysis of cases. Results Acceptance test, system accuracy test based on rules and performance test were realized. For the tests, 1,380 referral forms of real situations were used. Conclusion On the results obtained with the analysis of real data, ILITIA, the developed architecture has met the requirements to assist medical specialists on gestational risk classification, which decreases the inconvenience of pregnant women displacement and the resulting costs.

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Abstract Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.

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AbstractIntroduction: The maintenance of balance and body orientation during standing is essential to perform different activities. One of the devices used to measure balance them is the force platform. This device measures the ground reaction force (GRF) and displacement of the center of pressure (COP), both biomechanical parameters involved in human motion. This article proposes a new design for non-commercial low-cost force platforms for scientific research purposes. Methods for calibration and validation are also described. Methods A force platform, developed according to International Standards of Measurement and dedicated to measuring feet contact forces was built for approximately one tenth of the cost of commercial platforms. Calibration was performed by loading known masses, centralized or distributed, on the platform. An experimental study was conducted with four volunteers in different conditions to validate and verify the practical applicability of the device. Results The platform calibration showed an adequate connectivity, linearity and reliable measurement of the variables proposed in this research, being suitable for studies of human postural behavior. Conclusion Based on the validation results, we believe the low-cost platform can be used as stabilometric device to measure postural control and balance in clinical or sports experiments. However future studies will be required to provide a final validation and compare its performance with other force platforms.

Resumo em Inglês:

Abstract Introduction: The search for human brain templates has been progressing in the past decades and in order to understand disease patterns a need for a standard diffusion tensor imaging (DTI) dataset was raised. For this purposes, some DTI templates were developed which assist group analysis studies. In this study, complementary information to the most commonly used DTI template is proposed in order to offer a patient-specific statistical analysis on diffusion-weighted data. Methods 131 normal subjects were used to reconstruct a population-averaged template. After image pre processing, reconstruction and diagonalization, the eigenvalues and eigenvectors were used to reconstruct the quantitative DTI maps, namely fractional anisotropy (FA), mean diffusivity (MD), relative anisotropy (RA), and radial diffusivity (RD). The mean absolute error (MAE) was calculated using a voxel-wise procedure, which informs the global error regarding the mean intensity value for each quantitative map. Results the MAE values presented a low MAE estimate (max(MAE) = 0.112), showing a reasonable error measure between our DTI-USP-131 template and the classical DTI-JHU-81 approach, which also shows a statistical equivalence (p<0.05) with the classical DTI template. Hence, the complementary standard deviation (SD) maps for each quantitative DTI map can be added to the classical DTI-JHU-81 template. Conclusion In this study, variability DTI maps (SD maps) were reconstructed providing the possibility of a voxel-wise statistical analysis in patient-specific approach. Finally, the brain template (DTI-USP-131) described here was made available for research purposes on the web site (http://dx.doi.org/10.17632/br7bhs4h7m.1), being valuable to research and clinical applications.