Implementation of N-Interval fourier transform analysis - Application to compound action potentials

Abstract

N-Interval Fourier Transform Analysis (N-FTA) allows for spectral separation of a periodic target signal from uncorrelated background interference. A N-FTA pseudo-code is presented. The spectral resolution is defined by the repetition rate of the near periodic signal. Acceptance criteria for spectral targets were defined such that the probability of accepting false positives is less than 1500. Simulated and recorded neural compound action potentials (CAPs) were investigated. Simulated data allowed for comparison with reference solutions demonstrating the stability of N-FTA at conditions being comparable to real world data. Background activity was assessed with small errors. Evoked target components were assessed down to power spectral density being approximately N times below the background level. Validation was completed investigating a measured CAP. In neurophysiological recordings, this approach allows for accurate separation of near periodic evoked activity from uncorrelated background activities for frequencies below 1kHz. • N-FTA allows for spectral separation of a periodic target signal from uncorrelated interference by analyzing a segment containing N target signal repetitions. • A MATLAB implementation of the algorithm is provided along with simulated and recorded data. • N-FTA was successfully validated using simulated and measured data for CAPs.

Keywords

• N-Interval Fourier Transform Analysis (N-FTA)