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Robust unsupervised detection of action potentials with probabilistic models

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Benítez Iglesias, RaúlMés informacióMés informacióMés informació
Nenadic, Zoran
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 property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
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 
URIhttp://hdl.handle.net/2117/22779
ISSN0018-9294
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