A Novel Method for Electrophysiological Analysis of EMG Signals Using MesaClip

In electrophysiology, many methods have been proposed for the analysis of action potential firing frequencies. The aim of this study was to present an algorithm developed for a continuous wavelet transform that enables the filtering out of frequencies contributing to the shapes of action potentials (spikes), whilst retaining the frequencies that encode the periodicity of spike trains. The continuous wavelet transform allows us to decompose a signal into its constituent frequencies. A signal with a single event, such as a spike, is composed of frequencies that characterize the shape of the spike. A signal with two spikes will also be composed of frequencies characterizing the shape of the action potential, but in addition will include a substantial portion of its power at the frequency corresponding to the time-difference between the two spikes. This is achieved by clipping peaks from the wavelet amplitudes that are narrower than a given minimum number of phase cycles. We present some application examples in both synthetic signals and electrophysiological recordings. This new approach can provide a major new analytical tool for analysis of electrophysiological signals.