Robust unsupervised detection of action potentials with probabilistic models
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Document typeArticle
Defense date2008-04-30
PublisherInstitute of Electrical and Electronics Engineers
Rights accessRestricted access - publisher's policy
All rights reserved. This work is protected by the corresponding intellectual and industrial
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Abstract
We develop a robust and fully unsupervised algorithm
for the detection of action potentials from extracellularly recorded
data. Using the continuous wavelet transform allied to probabilistic
mixture models and Bayesian probability theory, the detection of
action potentials is posed as a model selection problem. Our technique
provides a robust performance over a wide range of simulated
conditions, and compares favorably to selected supervised
and unsupervised detection techniques.
CitationBenítez, R.; Nenadic, Z.(2008).Robust unsupervised detection of action potentials with probabilistic models. IEEE Transactions on Biomedical Engineering, 55 (4): 1344-1354. ISSN:0018-9294
ISSN0018-9294
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