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On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings

On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings We present an open, parametric system for automatic detection of EEG artifacts in polysomnographic recordings. It relies on independent parameters reflecting the relative presence of each of the eight types of artifacts in a given epoch. An artifact is marked if any of these parameters exceeds a threshold. These thresholds, set for each parameter separately, can be adjusted via “learning by example” procedure (multidimensional minimization with computationally intensive cost function), which can be used to automatically tune the parameters to new types of datasets, environments or requirements. Performance of the system, evaluated on 103 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the inter-expert agreement. To make this statement well defined, we review the methodology of evaluation for this kind of detection systems. Complete source code is available from http://eeg.pl ; a user-friendly version with Java interface is available from http://signalml.org . http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings

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Publisher
Springer Journals
Copyright
Copyright © 2009 by Humana Press Inc.
Subject
Biomedicine; Computational Biology/Bioinformatics; Biotechnology; Neurology ; Computer Appl. in Life Sciences ; Neurosciences
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1007/s12021-009-9045-2
pmid
19308339
Publisher site
See Article on Publisher Site

Abstract

We present an open, parametric system for automatic detection of EEG artifacts in polysomnographic recordings. It relies on independent parameters reflecting the relative presence of each of the eight types of artifacts in a given epoch. An artifact is marked if any of these parameters exceeds a threshold. These thresholds, set for each parameter separately, can be adjusted via “learning by example” procedure (multidimensional minimization with computationally intensive cost function), which can be used to automatically tune the parameters to new types of datasets, environments or requirements. Performance of the system, evaluated on 103 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the inter-expert agreement. To make this statement well defined, we review the methodology of evaluation for this kind of detection systems. Complete source code is available from http://eeg.pl ; a user-friendly version with Java interface is available from http://signalml.org .

Journal

NeuroinformaticsSpringer Journals

Published: Mar 24, 2009

References