Detecting slow wave sleep using a single EEG signal channel

Su, Bo-Lin; Luo, Yuxi; Hong, Chih-Yuan; Nagurka, Mark L.; Yen, Chen‐Wen

doi:10.1016/j.jneumeth.2015.01.023

Cited by 18 publications

(13 citation statements)

References 31 publications

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“…Various running average factor values were tested, and a value of 8 provided the best balance of sensitivity and specificity. In our participants, these settings resulted in a sensitivity of 97±3% and a specificity of 83±11% compared to the gold standard of visual scoring, indicating that, on an epoch-by-epoch basis, the ability of the protocol for discriminating SWS from other sleep-wake stages is excellent (Figure 2B), and superior to prior automated SWS scoring algorithms (Su et al, 2015). …”

Section: Methodsmentioning

confidence: 70%

Automated selective disruption of slow wave sleep

Zempel

Holtzman

2017

Journal of Neuroscience Methods

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Background Slow wave sleep (SWS) plays an important role in neurophysiologic restoration. Experimentally testing the effect of SWS disruption previously required highly time-intensive and subjective methods. Our goal was to develop an automated and objective protocol to reduce SWS without affecting sleep architecture. New Method We developed a custom Matlab™ protocol to calculate electroencephalogram spectral power every 10 seconds live during a polysomnogram, exclude artifact, and, if measurements met criteria for SWS, deliver increasingly louder tones through earphones. Middle-aged healthy volunteers (n=10) each underwent 2 polysomnograms, one with the SWS disruption protocol and one with sham condition. Results The SWS disruption protocol reduced SWS compared to sham condition, as measured by spectral power in the delta (0.5–4 Hz) band, particularly in the 0.5–2 Hz range (mean 20% decrease). A compensatory increase in the proportion of total spectral power in the theta (4–8 Hz) and alpha (8–12 Hz) bands was seen, but otherwise normal sleep features were preserved. N3 sleep decreased from 20±34 to 3±6 minutes, otherwise there were no significant changes in total sleep time, sleep efficiency, or other macrostructural sleep characteristics. Comparison with existing method This novel SWS disruption protocol produces specific reductions in delta band power similar to existing methods, but has the advantage of being automated, such that SWS disruption can be performed easily in a highly standardized and operator-independent manner. Conclusion This automated SWS disruption protocol effectively reduces SWS without impacting overall sleep architecture.

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Section: Methodsmentioning

confidence: 70%

Automated selective disruption of slow wave sleep

Zempel

Holtzman

2017

Journal of Neuroscience Methods

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“…Previous small‐sample‐size studies have evaluated automated scoring software using single‐channel EEG data (Fietze et al., ; Fraiwan, Lweesy, Khasawneh, Wenz, & Dickhaus, ; Garcia‐Molina et al., ; Lucey et al., ; Su, Luo, Hong, Nagurka, & Yen, ; Zhang & Wu, ; Zhu, Li, & Wen, ). In 15 healthy adults.…”

Section: Discussionmentioning

confidence: 99%

“…Previous small-sample-size studies have evaluated automated scoring software using single-channel EEG data (Fietze et al, 2015;Fraiwan, Lweesy, Khasawneh, Wenz, & Dickhaus, 2012;Garcia-Molina et al, 2015;Lucey et al, 2016;Su, Luo, Hong, Nagurka, & Yen, 2015;Zhang & Wu, 2018;Zhu, Li, & Wen, 2014 patients. However, the agreement was slightly lower for the singlelead EEG by itself than when EOG and EMG data were added to the analysis (Fietze et al, 2015).…”

Section: Ahi Calculationmentioning

confidence: 99%

Automatic identification of sleep and wakefulness using single‐channel EEG and respiratory polygraphy signals for the diagnosis of obstructive sleep apnea

Sabil

Vanbuis

Baffet

et al. 2018

Journal of Sleep Research

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Polysomnography (PSG) is necessary for the accurate estimation of total sleep time (TST) and the calculation of the apnea–hypopnea index (AHI). In type III home sleep apnea testing (HSAT), TST is overestimated because of the lack of electrophysiological sleep recordings. The aim of this study was to evaluate the accuracy and reliability of a novel automated sleep/wake scoring algorithm combining a single electroencephalogram (EEG) channel with actimetry and HSAT signals. The study included 160 patients investigated by PSG for suspected obstructive sleep apnea (OSA). Each PSG was recorded and scored manually using American Academy of Sleep Medicine (AASM) rules. The automatic sleep/wake‐scoring algorithm was based on a single‐channel EEG (FP2‐A1) and the variability analysis of HSAT signals (airflow, snoring, actimetry, light and respiratory inductive plethysmography). Optimal detection thresholds were derived for each signal using a training set. Automatic and manual scorings were then compared epoch by epoch considering two states (sleep and wake). Cohen's kappa coefficient between the manual scoring and the proposed automatic algorithm was substantial, 0.74 ± 0.18, in separating wakefulness and sleep. The sensitivity, specificity and the positive and negative predictive values for the detection of wakefulness were 76.51% ± 21.67%, 95.48% ± 5.27%, 81.84% ± 15.42% and 93.85% ± 6.23% respectively. Compared with HSAT signals alone, AHI increased by 22.12% and 27 patients changed categories of OSA severity with the automatic sleep/wake‐scoring algorithm. Automatic sleep/wake detection using a single‐channel EEG combined with HSAT signals was a reliable method for TST estimation and improved AHI calculation compared with HSAT.

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“…However, most such methods require a full polysomnography (PSG) set-up. Attempts to use single-channel EEG based automated sleep staging in humans have achieved a sensitivity index of 75–88% 5,11,16,35 , however none of these in humans used intracranial EEG (Table 2). …”

Section: Introductionmentioning

confidence: 99%

Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings

Reed

Birch

Kamiński

et al. 2017

Journal of Neuroscience Methods

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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 staging based on intracranial recordings alone. New Method Here we show a reliable automated method for the detection of periods of SWS solely based on intracranial EEG recordings. The method is utilizes the ratio of spectral power in delta, theta, and spindle frequencies relative to alpha and beta frequencies to classify 30-second segments as SWS or not. Results We evaluated this new method by comparing its performance against visually scored patients (n=9), in which we also recorded EOG and EMG simultaneously. Our method had a mean positive predictive value of 64% across all nights. Also, an ROC analysis of the performance of our algorithm compared to manually labeled nights revealed a mean average area under the curve of 0.91 across all nights. Comparison with existing method Our method had an average kappa score of 0.72 when compared to visual sleep scoring by an independent blinded sleep scorer. Conclusion This shows that this simple method is capable of differentiating between SWS and non-SWS epochs reliably based solely on intracranial EEG recordings.

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Detecting slow wave sleep using a single EEG signal channel

Cited by 18 publications

References 31 publications

Automated selective disruption of slow wave sleep

Automated selective disruption of slow wave sleep

Automatic identification of sleep and wakefulness using single‐channel EEG and respiratory polygraphy signals for the diagnosis of obstructive sleep apnea

Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings

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