Automatic extracellular spike detection with piecewise optimal morphological filter

Neuronal spike detection is a technical challenge because of large amounts of background noise and contributions of many neurons to recorded signals. In this paper, we propose an automatic spike detection algorithm in which piecewise optimal morphological filters are designed to separate action potentials (spikes) from background noise. The structure elements of morphological filters are constructed with Gaussian function and a concise criterion is introduced to piecewise optimized structure elements. An adaptive amplitude threshold is utilized to detect spike events when the spikes are extracted by the morphological filter, which increases the detection accuracy. We evaluate our algorithm with both synthesized neural recordings and real neural data, and compare it with two established spike detection methods.