RESUMEN
In motor nerve conduction studies, important diagnostic information is provided by the late-wave responses, comprised of F-waves, A-waves, and repeaters. Late-waves in addition to contamination from power line interference and baseline disturbance, are of low amplitude and random in nature. This makes computer-based analysis of late-wave activity very challenging, especially the computer-based F-wave onset latency assignment. Currently available algorithms assign latency independently on a trace-by-trace basis without considering the information present in an entire ensemble of traces. A novel algorithm that takes into account the ensemble information for segmenting and classifying regions of late-wave data is described in this paper, which in turn can be used to improve the performance of computer-based F-wave onset latency assignment.
Asunto(s)
Potenciales de Acción/fisiología , Algoritmos , Electrofisiología/métodos , Neuronas Motoras/clasificación , Neuronas Motoras/fisiología , Humanos , Procesamiento de Imagen Asistido por Computador , Conducción Nerviosa/fisiologíaRESUMEN
The health of peripheral nerves is commonly assessed using nerve conduction studies, in which the nerve is stimulated electrically and the evoked responses are analyzed. In motor nerve studies, important diagnostic information is provided by the late-wave responses, comprised of F-waves, A-waves and repeaters. Computer-based analysis of late-wave activity is challenging due to the random nature and low amplitude of these signals. This paper describes an algorithm for segmenting portions of late-wave activity and classifying the type of evoked response. The algorithm may be used to improve the robustness of computer-based late-wave assessment.