Towards automatic classification of pathological epileptic tissue with interictal high frequency oscillations

AbstractHigh 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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Unsupervised Classification of High-Frequency Oscillations in Human Neocortical Epilepsy and Control Patients

Journal of Neurophysiology ◽

10.1152/jn.01082.2009 ◽

2010 ◽

Vol 104 (5) ◽

pp. 2900-2912 ◽

Cited By ~ 90

Author(s):

Justin A. Blanco ◽

Matt Stead ◽

Abba Krieger ◽

Jonathan Viventi ◽

W. Richard Marsh ◽

...

Keyword(s):

High Frequency ◽

Unsupervised Classification ◽

Data Set ◽

High Frequency Oscillations ◽

Unsupervised Approach ◽

Brain States ◽

Neocortical Epilepsy ◽

Number Of Classes ◽

And Control

High-frequency oscillations (HFOs) have been observed in animal and human intracranial recordings during both normal and aberrant brain states. It has been proposed that the relationship between subclasses of these oscillations can be used to identify epileptic brain. Studies of HFOs in epilepsy have been hampered by selection bias arising primarily out of the need to reduce the volume of data so that clinicians can manually review it. In this study, we introduce an algorithm for detecting and classifying these signals automatically and demonstrate the tractability of analyzing a data set of unprecedented size, over 31,000 channel-hours of intracranial electroencephalographic (iEEG) recordings from micro- and macroelectrodes in humans. Using an unsupervised approach that does not presuppose a specific number of clusters in the data, we show direct evidence for the existence of distinct classes of transient oscillations within the 100- to 500-Hz frequency range in a population of nine neocortical epilepsy patients and two controls. The number of classes we find, four (three plus one putative artifact class), is consistent with prior studies that identify “ripple” and “fast ripple” oscillations using human-intensive methods and, additionally, identifies a less examined class of mixed-frequency events.

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A Neuromorphic Spiking Neural Network Detects Epileptic High Frequency Oscillations in the Scalp EEG

10.21203/rs.3.rs-1061301/v1 ◽

2021 ◽

Author(s):

Karla Burelo ◽

Georgia Ramantani ◽

Giacomo Indiveri ◽

Johannes Sarnthein

Keyword(s):

Low Power ◽

Seizure Frequency ◽

High Frequency ◽

Therapy Monitoring ◽

Non Invasive ◽

Scalp Eeg ◽

High Frequency Oscillations ◽

Eeg Recordings ◽

Active Epilepsy ◽

Wearable Electronic Devices

Abstract Background: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal architectures for being embedded in compact low-power signal processing hardware. Methods: We analyzed 20 scalp EEG recordings from 11 patients with pediatric focal lesional epilepsy. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. Results: We identified the optimal SNN parameters to automatically detect EoI and reject artifacts. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.83, p < 0.0001, Spearman’s correlation).Conclusions: The fully automated SNN detected clinically relevant HFO in the scalp EEG. This is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.

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High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1523583113 ◽

2016 ◽

Vol 113 (33) ◽

pp. 9363-9368 ◽

Cited By ~ 35

Author(s):

Michel Le Van Quyen ◽

Lyle E. Muller ◽

Bartosz Telenczuk ◽

Eric Halgren ◽

Sydney Cash ◽

...

Keyword(s):

High Frequency ◽

Large Scale ◽

Microelectrode Array ◽

Slow Wave Sleep ◽

Inhibitory Neurons ◽

Sleep Cycle ◽

High Frequency Oscillations ◽

Wake Patterns ◽

Fast Oscillations ◽

Array Recordings

Beta (β)- and gamma (γ)-oscillations are present in different cortical areas and are thought to be inhibition-driven, but it is not known if these properties also apply to γ-oscillations in humans. Here, we analyze such oscillations in high-density microelectrode array recordings in human and monkey during the wake–sleep cycle. In these recordings, units were classified as excitatory and inhibitory cells. We find that γ-oscillations in human and β-oscillations in monkey are characterized by a strong implication of inhibitory neurons, both in terms of their firing rate and their phasic firing with the oscillation cycle. The β- and γ-waves systematically propagate across the array, with similar velocities, during both wake and sleep. However, only in slow-wave sleep (SWS) β- and γ-oscillations are associated with highly coherent and functional interactions across several millimeters of the neocortex. This interaction is specifically pronounced between inhibitory cells. These results suggest that inhibitory cells are dominantly involved in the genesis of β- and γ-oscillations, as well as in the organization of their large-scale coherence in the awake and sleeping brain. The highest oscillation coherence found during SWS suggests that fast oscillations implement a highly coherent reactivation of wake patterns that may support memory consolidation during SWS.

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Scalp EEG high frequency oscillations as a biomarker of treatment response in epileptic encephalopathy with continuous spike-and-wave during sleep (CSWS)

Seizure ◽

10.1016/j.seizure.2019.05.023 ◽

2019 ◽

Vol 71 ◽

pp. 151-157 ◽

Cited By ~ 4

Author(s):

Dezhi Cao ◽

Yan Chen ◽

Jianxiang Liao ◽

Hiroki Nariai ◽

Lin Li ◽

...

Keyword(s):

Treatment Response ◽

High Frequency ◽

Epileptic Encephalopathy ◽

Scalp Eeg ◽

High Frequency Oscillations

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High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy

Scientific Reports ◽

10.1038/s41598-019-52700-w ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

Ece Boran ◽

Johannes Sarnthein ◽

Niklaus Krayenbühl ◽

Georgia Ramantani ◽

Tommaso Fedele

Keyword(s):

Seizure Frequency ◽

Epilepsy Surgery ◽

High Frequency ◽

Focal Epilepsy ◽

Low Noise ◽

Drug Resistant ◽

Scalp Eeg ◽

High Frequency Oscillations ◽

Custom Made ◽

Active Epilepsy

Abstract High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R2 = 0.80, p < 0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p = 0.0066, PPV = 86%, NPV = 80%, accuracy = 84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.

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High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence

Computational Intelligence and Neuroscience ◽

10.1155/2018/1638097 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Peter Höller ◽

Eugen Trinka ◽

Yvonne Höller

Keyword(s):

High Frequency ◽

Visual Detection ◽

Signal To Noise Ratio ◽

Physiological Activity ◽

Automated Detection ◽

Detection Algorithms ◽

Scalp Eeg ◽

High Frequency Oscillations ◽

Scalp Electroencephalogram ◽

Electroencephalogram Eeg

High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of implantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely available at low costs and does not bear any risks. However, the detection of HFOs on the scalp represents a challenge that was taken on so far mostly via visual detection. Visual detection of HFOs is, in turn, highly time-consuming and subjective. In this review, we discuss that automated detection algorithms for detection of HFOs on the scalp are highly warranted because the available algorithms were all developed for invasively recorded EEG and do not perform satisfactorily in scalp EEG because of the low signal-to-noise ratio and numerous artefacts as well as physiological activity that obscures the tiny phenomena in the high-frequency range.

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Blinded study: prospectively defined high frequency oscillations predict seizure outcome in individual patients

10.1101/2020.12.24.20248799 ◽

2020 ◽

Author(s):

V Dimakopoulos ◽

P Mégevand ◽

E Boran ◽

S Momjian ◽

M Seeck ◽

...

Keyword(s):

Epilepsy Surgery ◽

High Frequency ◽

Dwell Time ◽

Nrem Sleep ◽

Seizure Outcome ◽

Seizure Freedom ◽

University Hospitals ◽

High Frequency Oscillations ◽

Depth Electrodes

AbstractBackgroundInterictal high frequency oscillations (HFO) are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant HFO in intracranial EEG (iEEG) from MNI Montreal and tested it in iEEG from Zurich. We here validate the algorithm on iEEG recorded in an independent epilepsy center so that HFO analysis was blinded to seizure outcome.MethodsWe selected consecutive patients from Geneva University Hospitals who underwent resective epilepsy surgery with postsurgical follow-up > 12 months. We analyzed long-term iEEG recordings during non-rapid eye movement (NREM) sleep that we segmented into intervals of 5 min. HFOs were defined in the ripple (80-250 Hz) and the fast ripple (FR, 250-500 Hz) frequency band. Contacts with the highest rate of ripples co-occurring with FR (FRandR) designated the HFO area. If the HFO area was not fully resected and the patient suffered from recurrent seizures (ILAE 2-6), this was classified as a true positive (TP) prediction.ResultsWe included iEEG recordings from 16 patients (median age 32 y, range [18-53]) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 mo, range [12-55]). The HFO area had high test-retest reliability across intervals (median dwell time 95%). We excluded two patients with dwell time < 50% from further analysis.The HFO area was fully included in the resected volume in 2/4 patients who achieved postoperative seizure freedom (ILAE 1, specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (ILAE > 1, sensitivity 90%), leading to an accuracy of 79%.ConclusionsWe validated the automated procedure to delineate the clinical relevant HFO area in individual patients of an independently recorded dataset and achieved the same good accuracy as in our previous studies.SignificanceThe reproducibility of our results across datasets is promising for a multicienter study testing the clinical application of HFO detection to guide epilepsy surgery.

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Scalp EEG interictal high frequency oscillations as an objective EEG biomarker of infantile spasms

10.1101/2020.05.31.126573 ◽

2020 ◽

Author(s):

Hiroki Nariai ◽

Shaun A. Hussain ◽

Danilo Bernardo ◽

Hirotaka Motoi ◽

Masaki Sonoda ◽

...

Keyword(s):

Slow Wave ◽

High Frequency ◽

Slow Wave Sleep ◽

Wave Activity ◽

Infantile Spasms ◽

Slow Wave Activity ◽

List Type ◽

Normal Children ◽

High Frequency Oscillations ◽

Epileptic Spasms

ABSTRACTObjectiveTo investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms.MethodsWe retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000 Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500 Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4 Hz and 0.5-1 Hz as quantified with modulation indices (MIs).ResultsTwenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p < 0.001 by HFO rate; p < 0.01 by MIs at 3-4 Hz; p = 0.02 by MIs at 0.5-1 Hz). The presence of active spasms was predicted by the logistic regression models incorporating HFO-related metrics (AUC: 0.80-0.98) better than that incorporating hypsarrhythmia (AUC: 0.61). The predictive performance of the best model remained favorable (87.5% accuracy) after a cross-validation procedure.ConclusionsIncreased rate of HFOs and coupling between HFOs and SWA are associated with active epileptic spasms.SignificanceScalp-recorded HFOs may serve as an objective EEG biomarker for active epileptic spasms.HighlightsObjective analyses of scalp high frequency oscillations and its coupling with slow-wave activity in infantile spasms were feasible.Increased rate of high frequency oscillations and its coupling with slow-wave activity correlated with active epileptic spasms.The scalp high frequency oscillations were also detected in neurologically normal children (although at the low rate).

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