Robust unsupervised detection of action potentials with probabilistic models

All of RECERCAT

To access the full text documents, please follow this link: http://hdl.handle.net/2117/22779

Title:	Robust unsupervised detection of action potentials with probabilistic models
Author:	Benítez Iglesias, Raúl; Nenadic, Zoran
Other authors:	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
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.
Abstract:	Peer Reviewed
Subject(s):	-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
Rights:
Document type:	Article - Updated version Article
Published by:	Institute of Electrical and Electronics Engineers
Share: