Application of new waveform analysis methods reflecting F-wave diversity -classification of F-wave diversity according to differences in the derived muscles.

Background: The F wave waveforms show diversity according to the number and size of re-firing cells, but there is still no analytical method that reflects this feature. We previously reported that five classifications of F waves are obtained from the ulnar nerve. However, the diversity of F waves derived from the lower extremities may not be similar. We therefore compared the diversity of F waves in the upper and lower extremities in healthy subjects. New method: F waves were measured during tibial nerve stimulation in 26 healthy subjects. The amount of amplitude decrease was calculated from the amplitude value after the additive averaging process and based on the average amplitude value of each stimulus, and the relationship between the peak latency and density was examined. Results: The amount of amplitude decrease due to the additive averaging process was negatively correlated with the density of negative peaks. The diversity of F waves could be categorized into four class based on the histograms. Comparison with existing method: The new method uses a novel additive average method that reflects the diversity of F waves. Furthermore, it uses a histogram to visualize the cancellation between waveforms. Conclusion: We developed an analysis method that reflects the diversity of F waves in a novel manner, which visualizes cancellation between waveforms using a histogram.

A new waveform analysis method reflecting the diversity of F-wave Waveforms-Waveform types in healthy subjects based on the combined use of the additive averaging method and histograms.

BACKGROUND: F-waves, which are an indicator of the excitability of spinal cord anterior horn cells, are characterized by diverse waveforms. However, no analytical method has yet been development that fully reflects the diversity of such waveforms. The present study examined whether or not the change in the amplitude by the additive averaging process reflects the dispersion of the peak. NEW METHOD: The average amplitude of each waveform and the decrease in the amplitude after the additive averaging process were determined. The correlation between the decrease in the amplitude and the density of the peak was then examined. The histogram was also used to classify the type of waveform dispersion based on the characteristics of the peak latency. RESULTS: No correlation was found between the change in the amplitude and the peak density. However, the F-waves obtained from the ulnar nerve of healthy subjects were able to be classified into five types. COMPARISON WITH EXISTING METHOD: The parameters of an F-wave analysis are the rise latency, the amplitude and the persistence, and many reports have examined F-waves based on the changes in these values. The present study explored new parameters focusing on the waveform of F-waves reflecting the motor unit. CONCLUSION: The results of this study may help to establish a standard of comparison when using the F wave to evaluate spasticity due to upper motor neuron disorders.