2020: Identification of Neurophysiological Biomarkers

Presented by Nathália Oliveira & Gabriel Lins: According to the World Health Organization (WHO), stroke stands out as the second leading cause of death in the world, responsible for approximately 6.7 million deaths in 2016. There is a tendency to remain in this position until the year 2030, accounting for about 12.2% of the predicted deaths. Being the disease with the highest prevalence of deaths in Brazil, it is also a substantial cause of disability in the world. The disease accounts for 10% of all deaths recorded in the country. In this scenario, low-cost neurophysiological evaluations such as quantitative electroencephalogram (qEEG) can be an outlet for a rapid and effective diagnostic and prognostic evaluation. Currently the standard is the use of computed tomography (CT) or magnetic resonance imaging (MRI) however both have a high cost, inaccessible to most cities in the country, in addition to the diagnostic resolution depend on time so that the lesion can be visualized being 2 to 6 hours on MRI and CT from 24 hours for good accuracy. In the EEG vascular lesions are significantly perceived by the disturbance in the brain waves and the preservation of rapid background activity is indicative of considerable neuronal survival in the infarction zone and, therefore, indicative of a good prognosis. Currently many qEEG studies are guiding interventions for post-stroke rehabilitation by neurofeedback. Neurofeedback is an adjunct treatment for cognitive and psychological dysfunctions arising from stroke, offering benefits for patients outside the time interval in which spontaneous improvement is expected. As a proposal for cognitive rehabilitation, neurofeedback may favor attention rehabilitation, language processing and working memory. Recent studies have shown that Neurofeedback can be effective in the treatment of different clinical manifestations, including stroke, but is underutilized in the clinic of knowledge about the potential efficacy of this approach in post-stroke rehabilitation. It is estimated, however, using more complex tools by Artificial Intelligence (AI) such as Machine learning, Fuzzy Approximate Entropy, sLORETA among others, added to clinical information, depending on the demand for sensitivity and specificity of the system developed to structure high-precision classification parameters in diagnostic and prognostic evaluation, enabling better results in interventions.

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Presented by Nathália Oliveira & Gabriel Lins: According to the World Health Organization (WHO), stroke stands out as the second leading cause of death in the world, responsible for approximately 6.7 million deaths in 2016. There is a tendency to remain in this position until the year 2030, accounting for about 12.2% of the predicted deaths. Being the disease with the highest prevalence of deaths in Brazil, it is also a substantial cause of disability in the world. The disease accounts for 10% of all deaths recorded in the country. In this scenario, low-cost neurophysiological evaluations such as quantitative electroencephalogram (qEEG) can be an outlet for a rapid and effective diagnostic and prognostic evaluation. Currently the standard is the use of computed tomography (CT) or magnetic resonance imaging (MRI) however both have a high cost, inaccessible to most cities in the country, in addition to the diagnostic resolution depend on time so that the lesion can be visualized being 2 to 6 hours on MRI and CT from 24 hours for good accuracy. In the EEG vascular lesions are significantly perceived by the disturbance in the brain waves and the preservation of rapid background activity is indicative of considerable neuronal survival in the infarction zone and, therefore, indicative of a good prognosis. Currently many qEEG studies are guiding interventions for post-stroke rehabilitation by neurofeedback. Neurofeedback is an adjunct treatment for cognitive and psychological dysfunctions arising from stroke, offering benefits for patients outside the time interval in which spontaneous improvement is expected. As a proposal for cognitive rehabilitation, neurofeedback may favor attention rehabilitation, language processing and working memory. Recent studies have shown that Neurofeedback can be effective in the treatment of different clinical manifestations, including stroke, but is underutilized in the clinic of knowledge about the potential efficacy of this approach in post-stroke rehabilitation. It is estimated, however, using more complex tools by Artificial Intelligence (AI) such as Machine learning, Fuzzy Approximate Entropy, sLORETA among others, added to clinical information, depending on the demand for sensitivity and specificity of the system developed to structure high-precision classification parameters in diagnostic and prognostic evaluation, enabling better results in interventions.

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2020: Identification of Neurophysiological Biomarkers
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