title: Robust unsupervised detection of action potentials with probabilistic models.
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.
authors.affiliation: Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA. raul.benitez@upc.edu
authors.forename: Raul
authors.initials: R
authors.lastname: Benitez
authors.forename: Zoran
authors.initials: Z
authors.lastname: Nenadic
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted