Automated detection of EEG artifacts during sleep: Preprocessing for all-night spectral analysis

Ktonas, P.Y.; Osório, Paulo Léo Manassi; Everett, Robert L.

doi:10.1016/0013-4694(79)90139-1

Cited by 13 publications

(4 citation statements)

References 10 publications

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“…Our aim was not to identify particular types of artefacts like, for example, contamination by eye movements, but to establish a reliable procedure to exclude artefacts to be able to obtain reproducible clean quantitative EEG measures as, for example, mean power density spectra, circumventing manual artefact scoring, which is time consuming and to some degree subjective (Anderer et al., ; Coppieters't Wallant et al., ). Many previous papers focused on a specific algorithm (Coppieters't Wallant et al., ; D'Rozario et al., ) or reviewed approaches more generally, not assessing their performance or did not provide parameters that could be applied (Barlow, , , ; Bodenstein & Praetorius, ; Durka et al., ; Gotman et al., ; Ktonas, Osorio, & Everett, ).…”

Section: Discussionmentioning

confidence: 99%

“…Third column: power thresholding 25-90 Hz (PT25). With ATf less high-frequency artefacts were removed than by expert scoring (red curve above green one), while the two other methods removed more high-frequency artefacts than by expert scoring parameters that could be applied (Barlow, 1983(Barlow, , 1984(Barlow, , 1986Bodenstein & Praetorius, 1977;Durka et al, 2003;Gotman et al, 1981;Ktonas, Osorio, & Everett, 1979).…”

Section: Discussionmentioning

confidence: 99%

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Automatic artefact detection in single‐channel sleep EEG recordings

Malafeev

Omlin

Wierzbicka

et al. 2018

Journal of Sleep Research

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Quantitative electroencephalogram analysis (e.g. spectral analysis) has become an important tool in sleep research and sleep medicine. However, reliable results are only obtained if artefacts are removed or excluded. Artefact detection is often performed manually during sleep stage scoring, which is time consuming and prevents application to large datasets. We aimed to test the performance of mostly simple algorithms of artefact detection in polysomnographic recordings, derive optimal parameters and test their generalization capacity. We implemented 14 different artefact detection methods, optimized parameters for derivation C3A2 using receiver operator characteristic curves of 32 recordings, and validated them on 21 recordings of healthy participants and 10 recordings of patients (different laboratory) and considered the methods as generalizable. We also compared average power density spectra with artefacts excluded based on algorithms and expert scoring. Analyses were performed retrospectively. We could reliably identify artefact contaminated epochs in sleep electroencephalogram recordings of two laboratories (healthy participants and patients) reaching good sensitivity (specificity 0.9) with most algorithms. The best performance was obtained using fixed thresholds of the electroencephalogram slope, high-frequency power (25-90 Hz or 45-90 Hz) and residuals of adaptive autoregressive models. Artefacts in electroencephalogram data can be reliably excluded by simple algorithms with good performance, and average electroencephalogram power density spectra with artefact exclusion based on algorithms and manual scoring are very similar in the frequency range relevant for most applications in sleep research and sleep medicine, allowing application to large datasets as needed to address questions related to genetics, epidemiology or precision medicine.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Automatic artefact detection in single‐channel sleep EEG recordings

Malafeev

Omlin

Wierzbicka

et al. 2018

Journal of Sleep Research

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“…Selection of epoch length is an important but often overlooked issue in EEG analysis. In the early studies, when the technology for EEG analysis was not widely deployed, polysomnographic variables were monitored, and sleep was scored in 30-s epochs by standard criteria regardless of the subject investigated; [5][6][7][8] some researchers even went to the length of utilizing 60-s epochs. 9 As a natural outgrowth of the technology, shorter epochs were introduced for analysis.…”

Section: Introductionmentioning

confidence: 99%

Selection of optimal epoch duration in assessment of rodent sleep-wake profiles

Yan¹,

Xu²,

Huang

et al. 2011

Sleep and Biological Rhythms

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One of the major inconveniences encountered in sleep studies is the time-consuming labor involved in electroencephalography (EEG) analysis. The choice of epoch duration is of prime importance and is highly related to the outcome of EEG analysis. This study was designed to find the optimal EEG epoch duration that accurately reveals the sleep-wake profiles of animals and relieve researchers of the laborious analysis of rodent sleep-wake parameters. We analyzed mouse and rat EEG signals with commonly used epoch durations (4, 8, 10, 20, and 30 s) and compared the resulting sleep-wake profiles in terms of amounts of sleep and wakefulness, number and duration of episodes, number of sleep-wake stage transitions, and EEG power spectra. There were no statistical differences in the amount and EEG power density of wakefulness, rapid eye movement (REM) and non-REM sleep among the 5 epoch durations we used. However, the shorter the epoch, the more numerous the stage transitions and the larger the episode number. The purpose of an experiment should be a main criterion for determining the optimal epoch length for the experiment. When the amount of each stage and power density of EEG signals are desired, a 30-s epoch duration is appropriate, to save time; and the results obtained are no different from those found by analysis of other epoch durations. If stage transition and duration and number of episodes are to be considered, a 4-s epoch duration is suggested as the best choice for analysis.

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“…One of the first models used to describe artifact-free EEG data was based on the assumption of Gaussian amplitude distribution in the undisturbed EEG. Ktonas et al [37], for instance, defined departures from a normal amplitude distribution, evaluated by a ¯2 test, as artifacts in sleep EEG data. Already in 1977, Gevins et al [38] described an artifact identification method based on departures from features calibrated from short visually evaluated artifact-free data segments.…”

Section: Artifact Identificationmentioning

confidence: 99%

Artifact Processing in Computerized Analysis of Sleep EEG – A Review

Anderer¹,

Roberts²,

Schlögl³

et al. 1999

Neuropsychobiology

139

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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.

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Automated detection of EEG artifacts during sleep: Preprocessing for all-night spectral analysis

Cited by 13 publications

References 10 publications

Automatic artefact detection in single‐channel sleep EEG recordings

Automatic artefact detection in single‐channel sleep EEG recordings

Selection of optimal epoch duration in assessment of rodent sleep-wake profiles

Artifact Processing in Computerized Analysis of Sleep EEG – A Review

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