RESUMO
Los potenciales evocados auditivos del tronco cerebral (PEATC) son frecuentemente usados para fines diagnósticos; sin embargo, su procesamiento se hace difícil porque están inmersos en una gran cantidad de ruido proveniente no solo de fuentes externas sino también fisiológicas. Hasta ahora el método más utilizado y aceptado para obtener un registro confiable es la promediación coherente, aunque presenta algunos inconvenientes. La descomposición modal empírica (EMD) es una técnica relativamente nueva que se usa para el procesamiento de señales no estacionarias como la mayoría de señales fisiológicas. Este método separa una señal, extrayendo la energía asociada a diferentes escalas de tiempo intrínsecas, en una suma finita de modos oscilatorios. El propósito de este trabajo fue evaluar la EMD como una herramienta para mejorar el desempeño de la promediación coherente de PEATC buscando reducir la cantidad de épocas necesarias para obtener un registro confiable. Para tal fin se analizó la reconstrucción de ocho registros usando solamente los modos 2, 3 y 4 resultantes de la EMD, los estudios determinaron que una reconstrucción de 800 épocas es aceptable.
The brainstem auditory evoked potentials (BAEPs) are commonly used for diagnostic purposes; however, processing becomes difficult because they are immersed in a large amount of noise coming not only from external sources but also from other physiological sources. So far the most widely used and accepted method to obtain reliable recording is the coherent averaging, but this type of processing presents some drawbacks. The empirical mode decomposition (EMD) is a relatively new technique which is used for processing of non-stationary signal like almost physiological signals. This method separates a signal, xextracting the energy associated with various intrinsic time scales, into a finite set of oscillatory modes. The purpose of the study was to asssess the EMD as a tool for improving the performance of the averaging coherent BAEPs seeking to reduce the amount of epochs needed to obtain a reliable register. To this end, we have analyzed the reconstruction of eight registers using only modes 2, 3 and 4 resulting from the EMD, the studies found that a reconstruction of 800 epochs is acceptable.
RESUMO
The goal of this preliminary study was to investigate the feasibility of using singular value decomposition (SVD) to eliminate the M-wave from the surface electromyogram (EMG) of an electrically stimulated paretic muscle in order to extract the volitional response. An SVD-based algorithm combining the subspaces method and a subsequent filtering is presented. It was evaluated with EMG signals registered from surface of electrically stimulated muscles with simulated paresis and its performance was compared with a conventional fixed filter. The filtering strategy proposed showed a good performance in static conditions where there were no traces of the M-wave. In dynamic conditions, the SVD-based algorithm was robust but with some remaining M-wave traces. It would be as a consequence of modifications in the data matrix and, therefore, in the subspaces generator columns and the singular values. In general, the fixed filter was very sensitive to input signal disturbances. In all of these conditions there was a greater power reduction for the SVD-based filter than for the fixed filter.