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Robust unsupervised detection of action potentials with probabilistic models
Benítez Iglesias, Raúl; Nenadic, Zoran
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. NOLIN - Física No-Lineal i Sistemes Fora de l'Equilibri
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.
Peer Reviewed
Àrees temàtiques de la UPC::Matemàtiques i estadística::Probabilitat
Bayesian statistical decision theory
Action potentials
Bayesian probability theory
Continuous wavelet transform
Expectation maximization algorithm
Finite mixture models
Maximum likelihood principle
Estadística bayesiana
info:eu-repo/semantics/updatedVersion
Artículo
Institute of Electrical and Electronics Engineers
         

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