Analysis of raw microneurographic recordings based on wavelet de-noising technique and classification algorithm: wavelet analysis in microneurography.

We propose a new technique for analyzing the raw neurogram which enables the study of the discharge behavior of individual and group neurons. It utilizes an ideal bandpass filter, a modified wavelet de-noising procedure, an action potential detector, and a waveform classifier. We validated our approach with both simulated data generated from muscle sympathetic neurograms sampled at high rates in five healthy subjects and data recorded from seven healthy subjects during lower body negative pressure suction. The modified wavelet method was superior to the classical discriminator method and the regular wavelet de-noising procedure when applied to simulated neuronal signals. The detected spike rate and spike amplitude rate of the action potentials correlated strongly with number of bursts detected in the integrated neurogram (r = 0.79 and 0.89, respectively, p < 0.001). Eight major action potential waveform classes were found to describe more than 81% of all detected action potentials in all subjects. One class had characteristics similar in shape and in average discharge frequency (27.4 +/- 5.1 spikes/min during resting supine position) to those of reported single vasoconstrictor units. The newly proposed technique allows a precise estimate of sympathetic nerve activity and characterization of individual action potentials in multiunit records.