2017
DOI: 10.1016/j.jneumeth.2017.02.009
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Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings

Abstract: Background An automated process for sleep staging based on intracranial EEG data alone is needed to facilitate research into the neural processes occurring during slow wave sleep (SWS). Current manual methods for sleep scoring require a full polysomnography (PSG) set-up, including electrooculography (EOG), electromyography (EMG), and scalp electroencephalography (EEG). This set-up can be technically difficult to place in the presence of intracranial EEG electrodes. There is thus a need for a method for sleep s… Show more

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Cited by 23 publications
(22 citation statements)
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“…For example, a consistent line of research investigates the θ/β ratio as a potential biomarker for executive function, and in particular attentional processing ( Lubar, 1991 ; Angelidis et al, 2016 ; Gordon et al, 2018 ; van Son et al, 2019 ). Other work has explored using ratio measures in learning and memory ( Nokia et al, 2008 ; Kim et al, 2016 ; Trammell et al, 2017 ), age-related changes ( Matoušek and Petersén, 1973 ; Gasser et al, 1988 ; Clarke et al, 2001 ), and automated sleep scoring ( Costa-Miserachs et al, 2003 ; Krakovská and Mezeiová, 2011 ; Reed et al, 2017 ).…”
Section: Introductionmentioning
confidence: 99%
“…For example, a consistent line of research investigates the θ/β ratio as a potential biomarker for executive function, and in particular attentional processing ( Lubar, 1991 ; Angelidis et al, 2016 ; Gordon et al, 2018 ; van Son et al, 2019 ). Other work has explored using ratio measures in learning and memory ( Nokia et al, 2008 ; Kim et al, 2016 ; Trammell et al, 2017 ), age-related changes ( Matoušek and Petersén, 1973 ; Gasser et al, 1988 ; Clarke et al, 2001 ), and automated sleep scoring ( Costa-Miserachs et al, 2003 ; Krakovská and Mezeiová, 2011 ; Reed et al, 2017 ).…”
Section: Introductionmentioning
confidence: 99%
“…We extracted two sets of PIB features from the following bands: 0.5-5 Hz, 4-9Hz, 8-14Hz, 11-16Hz, 14-20Hz (low beta) and 20-30Hz (high beta). Frequency bands were selected based on prior automated sleep classification work using intracranial and scalp EEG [22][23][24][25]37,38,40] and preliminary analysis of patient H1. The first set of features was power in band relative to the power of the whole spectrum from 0.5 -30 Hz.…”
Section: Data Processingmentioning
confidence: 99%
“…Although clinical gold standard polysomnography involves trained experts reviewing scalp EEG recordings, prior research has shown the feasibility of automated behavioral state classification utilizing invasively recorded iEEG signals. These studies demonstrated the feasibility of classification of wakefulness and non-Rapid-Eye-Movement (NREM: N2 & N3) sleep stages [22][23][24][25]. However, the feasibility of REM classification and the impact of EBS induced iEEG artifacts on automated sleep scoring remains unclear.…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, in some studies it may be desirable to identify periods of NREM (Reed et al, 2017).…”
Section: Towards Automatic Classification Of Soz With Interictal Hfosmentioning
confidence: 99%