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

Reed, Chrystal M.; Birch, Kurtis; Kamiński, Jan; Sullivan, Shannon; Chung, Jeffrey M.; Mamelak, Adam N.; Rutishauser, Ueli

doi:10.1016/j.jneumeth.2017.02.009

Cited by 23 publications

(22 citation statements)

References 38 publications

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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%

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Donoghue

Dominguez

Voytek

2020

eNeuro

128

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Band ratio measures, computed as the ratio of power between two frequency bands, are a common analysis measure in neuro-electrophysiological recordings. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity. This assumes that the measure reflects relative powers of distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, the latter of which contributes power across all frequencies. Here, we investigate whether band ratio measures truly reflect oscillatory power differences, and/or to what extent ratios may instead reflect other periodic changes-such as in center frequency or bandwidth-and/or aperiodic activity. In simulation, we investigate how band ratio measures relate to changes in multiple spectral features, and show how multiple periodic and aperiodic features influence band ratio measures. We validate these findings in human electroencephalography (EEG) data, comparing band ratio measures to parameterizations of power spectral features, and find that multiple disparate features influence ratio measures. For example, the commonly applied theta / beta ratio is most reflective of differences in aperiodic activity, and not oscillatory theta or beta power. Collectively, we show that periodic and aperiodic features can create the same observed changes in band ratio measures, and that this is inconsistent with their typical interpretations as measures of periodic power. We conclude that band ratio measures are a non-specific measure, conflating multiple possible underlying spectral changes, and recommend explicit parameterization of neural power spectra as a more specific approach. Band Ratios 3 Significance Statement Neural oscillations are a ubiquitous feature of investigation in electrophysiological recordings. Frequency band ratio measures are a common approach to investigate neural oscillations, applied across cognitive and clinical neuroscience, and in recording modalities such as in electroencephalography and local field potentials. In this work we systematically investigate the methodological properties of band ratio measures. We show that band ratio measures are not specific to measuring oscillatory power, as they are intended and interpreted to do. Rather, they often reflect other features of the data, such as aperiodic, or 1/f-like, activity. These findings are significant for interpreting prior empirical and clinical research, guiding future work, and another motivation that aperiodic neural activity should be a key consideration when studying electrophysiological data.

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Section: Introductionmentioning

confidence: 99%

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Donoghue

Dominguez

Voytek

2020

eNeuro

128

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“…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%

Electrical Brain Stimulation and Continuous Behavioral State Tracking in Ambulatory Humans

Mívalt

Kremen

Sladky

et al. 2021

Preprint

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Electrical brain stimulation (EBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy and EBS therapies may actually further disturb normal sleep patterns and sleep quality. Novel devices capable of EBS and continuous intracranial EEG (iEEG) telemetry enable detailed assessments of therapy efficacy and tracking of sleep and comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT). The iEEG recorded from HPC are used to classify sleep during concurent ANT stimulation. Simultaneous polysomnography and HPC sensing was were used to train, validate and test an automated classifier for a range of ANT EBS frequencies (2 Hz, 7Hz, 100Hz, and 145 Hz). We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC sensing channel. The patient specific classifiers performed well under all thalamic EBS frequencies with an average F1-score 0.894, and provided viable classification into major sleep categories (Awake, NREM, REM). Within this project, we retrospectively analyzed classification performance with gold-standard polysomnography annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment. The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing EBS for epilepsy and associated sleep, cognitive and mood comorbidities.

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“…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%

Beyond rates: Time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone

Nunez

Charupanit

Sen‐Gupta

et al. 2020

Preprint

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High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection (Frauscher et al., 2017). However, previous research has shown that the number of HFOs per minute (i.e. the HFO "rate") is not stable over the duration of intracranial recordings. The rate of HFOs increases during periods of slow-wave sleep (von Ellenrieder et al., 2017), and HFOs that are predictive of epileptic tissue may occur in oscillatory patterns (Motoi et al., 2018). We sought to further understand how between-seizure (i.e. "interictal") HFO dynamics predict the seizure onset zone (SOZ). Using long-term intracranial EEG from 16 subjects, we fit Poisson and Negative Binomial mixture models that describe HFO dynamics and include the ability to switch between two discrete brain states. Oscillatory dynamics of HFO occurrences were found to be predictive of SOZ and were more consistently predictive than HFO rate. Using concurrent scalp-EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM (NREM) sleep and (2) awake and rapid eye movement (REM) sleep. This work suggests that unsupervised approaches for classification of epileptic tissue without sleep-staging can be developed using mixture modeling of HFO dynamics.

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

Cited by 23 publications

References 38 publications

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Electrical Brain Stimulation and Continuous Behavioral State Tracking in Ambulatory Humans

Beyond rates: Time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone

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