A Novel Matched Filter for Neural Action Potential Detection

Abstract

Detection of neural action potentials in background noise is the fist step to many neural researches. Matched filter is a simple and effective action potential detector, but it is found to be too sensitive to the amplitudes differences among the action potentials to be useful in general applications. By introducing adjust function, an improved matched filter detector is proposed. The detector eliminates the variance of differences among action potential amplitudes produced by matched filter. The method is tested and the results show that the improved matched filter outperforms classic matched filter method and energy operator method by getting higher correct ratio and lower false ratio.