On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings

Klekowicz, H.; Malinowska, Urszula; Piotrowska, A; Wołyńczyk-Gmaj, Dorota; Niemcewicz, Szymon; Durka, P.J.

doi:10.1007/s12021-009-9045-2

Cited by 12 publications

(11 citation statements)

References 23 publications

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“…Manual rejections of artifacts in dozens of overnight recordings would not fulfill the scientific criteria of repeatability, due to the low interexpert concordance. 26 To cope with this problem, we designed and implemented a custom, semiautomatic and algorithmic system, partially based on a previously implemented scheme. 26 Three main types of artifacts were considered:…”

Section: Artifacts Rejectionmentioning

confidence: 99%

See 1 more Smart Citation

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

Zieleniewska

Duszyk

Różański

et al. 2019

Int. J. Neur. Syst.

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We propose a fully parametric approach to the assessment of sleep architecture, based upon the classical electroencephalographic criteria, applicable also to the recordings of patients with disorders of consciousness (DOC). Sleep spindles and slow waves are automatically detected from the matching pursuit decomposition of overnight EEG recordings. Their evolution can be presented in the form of EEG profiles, yielding a continuous description of sleep architecture, compatible with the classical criteria used in sleep staging. We propose assessment of these EEG profiles by five parameters, which can be combined by a linear classifier, assessing the quality of sleep architecture. Proposed methodology is evaluated on 59 overnight EEG recordings from 19 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis according to the Coma Recovery Scale-Revised. Presented results indicate robustness of the proposed approach, which may serve as a valuable aid in diagnosis of DOC patients. Complete software environment for computing and presentation of EEG profiles is freely available from http://svarog.pl .

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Section: Artifacts Rejectionmentioning

confidence: 99%

“…26 To cope with this problem, we designed and implemented a custom, semiautomatic and algorithmic system, partially based on a previously implemented scheme. 26 Three main types of artifacts were considered:…”

Section: Artifacts Rejectionmentioning

confidence: 99%

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

Zieleniewska

Duszyk

Różański

et al. 2019

Int. J. Neur. Syst.

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show abstract

“…To deal with this situation, we have developed a custom system consisting of correction of ocular artifacts by Independent Component Analysis (ICA) and rejection of epochs contaminated by other artifacts, partially based on an earlier approach. 12…”

Section: Artifactsmentioning

confidence: 99%

Assessment of Statistically Significant Command-Following in Pediatric Patients with Disorders of Consciousness, Based on Visual, Auditory and Tactile Event-Related Potentials

Dovgialo

Chabuda

Duszyk

et al. 2019

Int. J. Neur. Syst.

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Disorders of consciousness (DOC) are among the major challenges of contemporary medicine, mostly due to the high rates of misdiagnoses in clinical assessment, based on behavioral scales. This turns our attention to potentially objective neuroimaging methods. Paradigms based on electroencephalography (EEG) are most suited for bedside applications, but sensitive to artifacts. These problems are especially pronounced in pediatric patients. We present the first study on the assessment of pediatric DOC patients by means of command-following procedures and involving long-latency cognitive event-related potentials. To deal with the above mentioned challenges, we construct a specialized signal processing scheme including artifact correction and rejection, parametrization, classification and final assessment of the statistical significance. To compensate for the possible bias of the tests involved in the final diagnosis, we propose the Monte Carlo evaluation of the processing pipeline. To compensate for possible sensory impairments of DOC patients, for each subject we check command-following responses to the stimuli in the major modalities: visual, tactile, and audio (words and sounds). We test the scheme on 20 healthy volunteers and present results for 15 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis on the Coma Recovery Scale-Revised (CRS-R).

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“…This is not much higher than the agreement between experts, and the disagreement may thus reflect the inherent difficulty of the task rather than differences in technique or approach by different observers. Klekowicz et al (2009) made 22 comparisons between expert classifications (artifact vs. neural) based on the EEG time series from seven polysomnographic recordings and found an agreement of 0.92 in terms of the area under curve of the best fitting ROC curve. Of the 22 comparisons, four were between classifications made by the same expert at different points in time.…”

Section: Performance Of Human Expertsmentioning

confidence: 99%

Classification of independent components of EEG into multiple artifact classes

2014

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Classification of independent components of EEG into multiple artifact classes AbstractIn this study, we aim to automatically identify multiple artifact types in EEG.We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five non-neural types of components.Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural vs. non-neural classifications, performance was on par with previous results obtained by others.We found that automatic separation of multiple artifact classes is possible with a small feature set.Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing them.

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On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings

Cited by 12 publications

References 23 publications

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

Assessment of Statistically Significant Command-Following in Pediatric Patients with Disorders of Consciousness, Based on Visual, Auditory and Tactile Event-Related Potentials

Classification of independent components of EEG into multiple artifact classes

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