RESUMEN
This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filter's performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either time invariant or time varying parameters. The best performance is achieved with a nonlinear model with time-varying parameters.
Asunto(s)
Electroencefalografía/métodos , Algoritmos , Mapeo Encefálico/métodos , Simulación por Computador , Hemodinámica , Humanos , Modelos Lineales , Imagen por Resonancia Magnética/métodos , Modelos Estadísticos , Método de Montecarlo , Distribución Normal , Reproducibilidad de los Resultados , Factores de TiempoRESUMEN
Fine-needle aspiration biopsy may provide miniscule material for diagnosis. A method was devised to ensure optimal retrieval of the specimen. If performed in the manner described, at least four to five different samples from each case may be obtained. This includes a smear stained with the Papanicolaou and hematoxylin and eosin methods, a cell block preparation, and at least two cytocentrifuge specimens. In 85 cases in which the method was applied and subsequently analyzed, we found the cell block and Papanicolaou-stained smears to be most effective for diagnosis, whereas the cytocentrifuge method was much less effective. The sensitivity and specificity were 93% and 100%, respectively.