Signal processing techniques for oscillatory potential extraction in the electroretinogram: automated highpass cutoff frequency estimation.

Abstract

Oscillatory potentials (OPs) are typically isolated from the electroretinogram (ERG) via linear, time-invariant, bandpass filtering. The use of a highpass cutoff frequency that is too low results in a- and b-wave contamination of the OP signal, while a cutoff frequency that is too high removes significant OP signal energy. Two methods for automated highpass cutoff frequency estimation were developed and evaluated. An OP amplitude analysis method exploited a trend in variation of maximum OP amplitude with cutoff frequency. A second method fit a time-varying exponential model to the rising edge of the b-wave and selected a cutoff frequency based on minimizing the error between the residual ERG signal (the signal formed by subtracting the OP signal from the original ERG signal) and the exponential fit. The performance of each method was evaluated at 11 luminances (0.001-100 scot cd · s/m(2)) in ten wild-type adult mice by comparing the automated selections to expert-selected highpass cutoff frequencies. It was noted that cutoff frequency selection was not critical at the lower luminance levels, but strongly influenced the OP signal shape for higher luminances. At the highest luminance, errors between the OP amplitude and exponential model versus expert selection were -6.3 ± 13 and -8.2 ± 7.3 Hz, respectively. ANOVAs showed that estimations made by the OP amplitude analysis method were generally statistically indistinguishable from the expert identifications. Furthermore, both OP amplitude analysis and exponential fitting error analysis provided excellent fits to the manual selections for the four highest stimulus luminance values.