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.
Escolá
Ricardo
R
CEA-LETI, Minatec, Grenoble, France. ricardo.escola@cea.fr
Bonnet
Stéphane
S
Guillemaud
Régis
R
Magnin
Isabelle
I
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D016477:Artifacts; D003058:Cockroaches; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods; D004594:Electrophysiology / Q000295:instrumentation*; D004867:Equipment Design; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net; D009474:Neurons / Q000473:pathology; D012815:Signal Processing, Computer-Assisted; D012984:Software