Wavelet-based scale-dependent detection of neurological action potentials

We study different wavelet-based algorithms for the detection of neurological action potentials recorded using micro-electrode arrays (MEA). We plan to develop a new family of ASIC-embedded low power algorithms close to the recording sites. We use the wavelet theory, not for previous-to-the- detection denoising stage (as it is usually used for) but for the detection itself. Different adaptive methods are presented with varying complexity levels. We demonstrate that wavelet-based detection of extracellular action potentials is superior than traditional and simpler approaches, at the expense of a slightly larger computational load. Moreover, our method is shown to be fully compatible with an embedded implementation. Proposed algorithms are applied to simulated datasets using a simplified model of the American cockroach antennal lobe.