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.

Facial affect processing inbipolar disorder:a magnetoencephalography study

Background: Identifying biological markers to aid diagnosis of bipolar disorder (BD) is critically important. To be considered a possible biological marker, neural patterns in BD should be discriminant from those in healthy individuals (HI). We examined patterns of neuromagnetic responses revealed by magnetoencephalography (MEG) during implicit emotion-processing using emotional (happy, fearful, sad) and neutral facial expressions, in sixteen BD and sixteen age- and gender-matched healthy individuals. Methods: Neuromagnetic data were recorded using a 306-channel whole-head MEG ELEKTA Neuromag System, and preprocessed using Signal Space Separation as implemented in MaxFilter (ELEKTA). Custom Matlab programs removed EOG and ECG signals from filtered MEG data, and computed means of epoched data (0-250ms, 250-500ms, 500-750ms). A generalized linear model with three factors (individual, emotion intensity and time) compared BD and HI. A principal component analysis of normalized mean channel data in selected brain regions identified principal components that explained 95% of data variation. These components were used in a quadratic support vector machine (SVM) pattern classifier. SVM classifier performance was assessed using the leave-one-out approach. Results: BD and HI showed significantly different patterns of activation for 0-250ms within both left occipital and temporal regions, specifically for neutral facial expressions. PCA analysis revealed significant differences between BD and HI for mild fearful, happy, and sad facial expressions within 250-500ms. SVM quadratic classifier showed greatest accuracy (84%) and sensitivity (92%) for neutral faces, in left occipital regions within 500-750ms. Conclusions: MEG responses may be used in the search for disease specific neural markers.

The association between spastic Cerebral Palsy, intellectual impairment, and gestational age: results from the Northern Ireland Cerebral Palsy Register

Background
Studies suggest a complex relationship between Cerebral Palsy sub-types, severity of impairment, and risk factors such as gestational age. To investigate these relationships, we conducted analyses on over 1,100 children included in the Northern Ireland Cerebral Palsy Register (NICPR) whose clinical CP subtype was Bilateral Spastic or Spastic Hemiplegia, and for whom information was available on the relevant variables.
Methods
We tested for the association between Bilateral and Hemiplegia subtypes, severe intellectual impairment, and gestational age (term; moderately preterm; very or extremely preterm) while controlling for gender, socio-economic deprivation, year of birth, and birth weight (using a standardized birth-weight score based on deviance from the birth weight average within each gestational age band). Severity of intellectual impairment was dichotomised (severe intellectual delay vs. moderate or no delay).
Results
Logistic regressions indicated a good fit of the model, and the predictors included explained approximately 19% of variability in the outcome. The results indicated a strong association between the Bilateral subtype and severe intellectual impairment: compared to children with the Hemiplegia subtype, those with Bilateral Spastic CP displayed a 10-fold increase in the odds of severe intellectual impairment. The results revealed a significant interaction between CP subtype and gestational age: for the Bilateral CP subtype, being born at term was associated with increased probability of severe intellectual impairment.
Discussion
Results are consistent with other studies (Hemming et al., 2008) in indicating that the likelihood of cognitive impairments increases with increasing gestational age at delivery of Bilateral Spastic CP children. The results are discussed in light of hypotheses that suggest the brain might be able to reorganise and compensate the effects of lesions and injuries when it is still less developed.