Robust Unsupervised Detection of Action Potentials With Probabilistic Models

Author

Benitez, Raul ; Nenadic, Zoran

Author_Institution

Univ. Politec. de Catalunya, Barcelona

Volume

55

Issue

4

fYear

2008

fDate

4/1/2008 12:00:00 AM

Firstpage

1344

Lastpage

1354

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.

Keywords

cellular biophysics; neurophysiology; probability; wavelet transforms; Bayesian probability theory; action potentials; continuous wavelet transform; extracellularly recorded data; fully unsupervised algorithm; model selection problem; probabilistic mixture models; robust unsupervised detection; Bayesian methods; Continuous wavelet transforms; Discrete wavelet transforms; Humans; Maximum likelihood detection; Microelectrodes; Neurons; Noise generators; Robustness; Wavelet transforms; Action potentials; Bayesian probability theory; continuous wavelet transform; expectation maximization algorithm; finite mixture models; maximum likelihood principle; receiver operating characteristic; unsupervised detection; Action Potentials; Algorithms; Artificial Intelligence; Brain; Computer Simulation; Data Interpretation, Statistical; Electrocardiography; Humans; Models, Neurological; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/TBME.2007.912433

Filename

4384314