Literature DB >> 10494051

Artifact processing in computerized analysis of sleep EEG - a review.

P Anderer1, S Roberts, A Schlögl, G Gruber, G Klösch, W Herrmann, P Rappelsberger, O Filz, M J Barbanoj, G Dorffner, B Saletu.   

Abstract

Quantitative analysis of sleep EEG data can provide valuable additional information in sleep research. However, analysis of data contaminated by artifacts can lead to spurious results. Thus, the first step in realizing an automatic sleep analysis system is the implementation of a reliable and valid artifact processing strategy. This strategy should include: (1) high-quality recording techniques in order to minimize the occurrence of avoidable artifacts (e.g. technical artifacts); (2) artifact minimization procedures in order to minimize the loss of data by estimating the contribution of different artifacts in the EEG recordings, thus allowing the calculation of the 'corrected' EEG (e.g. ocular and ECG interference), and finally (3) artifact identification procedures in order to define epochs contaminated by remaining artifacts (e.g. movement and muscle artifacts). Therefore, after a short description of the types of artifacts in the sleep EEG and some typical examples obtained in different sleep stages, artifact minimization and identification procedures will be reviewed.

Mesh:

Year:  1999        PMID: 10494051     DOI: 10.1159/000026613

Source DB:  PubMed          Journal:  Neuropsychobiology        ISSN: 0302-282X            Impact factor:   2.328


  12 in total

1.  On the robust parametric detection of EEG artifacts in polysomnographic recordings.

Authors:  H Klekowicz; U Malinowska; A J Piotrowska; D Wołyńczyk-Gmaj; Sz Niemcewicz; P J Durka
Journal:  Neuroinformatics       Date:  2009-03-24

Review 2.  A review of signals used in sleep analysis.

Authors:  A Roebuck; V Monasterio; E Gederi; M Osipov; J Behar; A Malhotra; T Penzel; G D Clifford
Journal:  Physiol Meas       Date:  2013-12-17       Impact factor: 2.833

3.  Evaluation of an automated pipeline for large-scale EEG spectral analysis: the National Sleep Research Resource.

Authors:  Sara Mariani; Leila Tarokh; Ina Djonlagic; Brian E Cade; Michael G Morrical; Kristine Yaffe; Katie L Stone; Kenneth A Loparo; Shaun M Purcell; Susan Redline; Daniel Aeschbach
Journal:  Sleep Med       Date:  2017-11-29       Impact factor: 3.492

4.  An automated algorithm to identify and reject artefacts for quantitative EEG analysis during sleep in patients with sleep-disordered breathing.

Authors:  Angela L D'Rozario; George C Dungan; Siobhan Banks; Peter Y Liu; Keith K H Wong; Roo Killick; Ronald R Grunstein; Jong Won Kim
Journal:  Sleep Breath       Date:  2014-09-16       Impact factor: 2.816

Review 5.  Functional source separation and hand cortical representation for a brain-computer interface feature extraction.

Authors:  Franca Tecchio; Camillo Porcaro; Giulia Barbati; Filippo Zappasodi
Journal:  J Physiol       Date:  2007-03-01       Impact factor: 5.182

6.  Weighted phase lag index stability as an artifact resistant measure to detect cognitive EEG activity during locomotion.

Authors:  Troy M Lau; Joseph T Gwin; Kaleb G McDowell; Daniel P Ferris
Journal:  J Neuroeng Rehabil       Date:  2012-07-24       Impact factor: 4.262

7.  Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI.

Authors:  Lukas Wiedemann; Jana Chaberova; Kyle Edmunds; Guðrún Einarsdóttir; Ceon Ramon; Paolo Gargiulo
Journal:  Eur J Transl Myol       Date:  2015-03-11

8.  Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal.

Authors:  Marcin Jurczak; Marcin Kołodziej; Andrzej Majkowski
Journal:  Front Neurosci       Date:  2022-02-11       Impact factor: 4.677

9.  Algorithm for automatic analysis of electro-oculographic data.

Authors:  Kati Pettersson; Sharman Jagadeesan; Kristian Lukander; Andreas Henelius; Edward Haeggström; Kiti Müller
Journal:  Biomed Eng Online       Date:  2013-10-25       Impact factor: 2.819

10.  A brain-computer-interface for the detection and modulation of gamma band activity.

Authors:  Neda Salari; Michael Rose
Journal:  Brain Sci       Date:  2013-11-18
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