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Seizure duration is associated with multiple timescales in interictal iEEG band power

T0 review · 2 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Seizure duration is not arbitrary: it tracks patient-specific cycles in interictal brain-signal power that run from minutes to days, and combinations of those cycles explain most of its variability.

desk verdict A plausible extension of the group's cycle work to seizure duration, but the permutation test misses model selection and the 'above chance' claim is likely overstated. read the letter →

arxiv 2504.17888 v1 pith:CWH6LNGL submitted 2025-04-24 q-bio.NC

Mariella Panagiotopoulou , Gabrielle M. Schroeder , Jess Blickwedel , Fahmida A Chowdhury , Beate Diehl , Jane de Tisi , John S. Duncan , Alison Cronie
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Jennifer Falconer Ryan Faulder Veronica Leach Shona Livingstone Rhys H. Thomas Peter N. Taylor Yujiang Wang
This is my paper · ORCID
classification q-bio.NC
keywords seizuredurationseverityinterictaliEEGbandpowercyclesempiricalmodedecompositioncircadianrhythmultradianlinear-circularregression
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper aims to establish that the length of an epileptic seizure, used here as a proxy for its severity, is partly set by slow rhythmic fluctuations in the brain's ongoing activity. Analysing days-long intracranial EEG from 20 people with epilepsy, the authors decompose median band power in five frequency bands into narrow-band cycles with periods from minutes to several days. They find that no single cycle predicts seizure duration well on its own, but a combination of cycles chosen per patient explains most of the variation: 70% of subject-specific models reached an adjusted $R^2$ above 60%, and roughly 80% of those passed a permutation test at $p<0.05$. If the association is real, interictal (between-seizure) brain rhythms could serve as a basis for forecasting seizure severity and for timing treatments to low-severity periods.

What carries the argument

The load-bearing object is the band-power cycle: the paper takes 30-second median log band power from five frequency bands ($\delta$, $\theta$, $\alpha$, $\beta$, $\gamma$), applies Multivariate Empirical Mode Decomposition (MEMD), a signal-decomposition method that jointly splits the multivariate power signals into aligned narrow-band intrinsic mode functions, and uses the Hilbert transform to assign each cycle an instantaneous phase at every seizure's onset. Those phases enter a linear-circular cosine regression, $\log(\text{duration}) = \mu + \sum_k \beta_k \cos(\phi_k - \phi_{k,0}) + \epsilon$, which is fitted as ordinary regression on $\cos\phi_k$ and $\sin\phi_k$ pairs. Group-LASSO selects which cycles matter, leave-one-out cross-validation and adjusted $R^2$ choose among twelve candidate models per subject, and a 500-iteration permutation test decides whether the chosen model beats chance.

What would settle it

Re-run the permutation test by shuffling seizure durations before the model-selection step, repeating the full twelve-model search on every shuffled dataset and comparing each subject's real best adjusted $R^2$ to the distribution of shuffled best values; if the real values sit inside that distribution, the above-chance claim collapses. A second independent check is to fit each subject's model on the first half of their seizures and predict the second half; near-zero predictive $R^2$ would show the association does not generalise beyond the fitted seizure set.

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Extended reading notes

Core claim

Within an individual patient, the duration of a seizure depends in part on the phase of several concurrently running cycles in interictal iEEG band power, with each patient having its own mixture of ultradian, circadian, and multi-day cycles. The paper establishes this in two stages: a rank linear-circular correlation shows weak to moderate links between seizure duration and the phase of individual cycles, and then linear-circular regression with group-LASSO variable selection, comparing twelve candidate models per subject, shows that combinations of cycles explain most of the variability. Concretely, 70% of the selected models across 20 subjects had an adjusted $R^2$ greater than 0.60, and about 80% of those were above chance by a permutation test. The authors present the relationship as associative rather than causal: the band-power cycles are most plausibly readouts of underlying oscillatory processes that also shape seizure severity, and the detected cycle mix is subject-specific.

Load-bearing premise

The above-chance claim rests on a permutation test that shuffles seizure durations while keeping the already-selected band-power cycles fixed, so it does not account for the prior search over twelve candidate models per subject; if that selection step inflates the null distribution, the reported significance is overstated.

Editorial extensions

If this is right

  • Seizure severity, measured as duration, is modulated by subject-specific cycles in interictal brain activity rather than being purely stochastic.
  • Interictal recordings, the stretches between seizures, carry exploitable information about how long a subsequent seizure will last.
  • There is no universal severity cycle: the models draw on ultradian, circadian, and slower (about 2–4 day) cycles in a patient-specific mix, so any forecasting approach must be personalised.
  • Because duration is only a proxy for severity, the same modelling pipeline translates to other electrographic severity markers, and the paper illustrates this for one delta-band peak marker.
  • Timing treatments to phases of predicted low severity, chronotherapy, becomes a plausible clinical direction if the associations hold in longer ambulatory recordings.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A stricter test the authors did not run, shuffling seizure durations before the twelve-model search rather than after model selection, would isolate how much of the reported significance owes to the screening step; I would expect the above-chance fraction to shrink, and that corrected fraction is the number to quote.
  • If the detected cycles are markers of metabolic or molecular oscillators, as the authors speculate, then adding non-EEG observables such as sleep stage or blood-glucose fluctuations into the same linear-circular framework is a natural extension that might lift accuracy for the patients whose models failed.
  • The subject-specific cycle mix suggests a clinical workflow distinct from seizure forecasting: instead of asking whether a seizure will occur, one could ask, conditional on occurrence, how severe it will be, which could change acute treatment thresholds; the paper stops at association, so a prospective predictive study is the logical next step.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The manuscript investigates whether interictal iEEG band-power cycles, extracted via multivariate empirical mode decomposition (MEMD), are associated with seizure duration (used as a proxy for severity) in 20 subjects with refractory focal epilepsy. For each subject, the authors build linear-circular regression models relating log seizure duration to the phases of selected intrinsic mode functions (IMFs), with group-LASSO variable selection and a comparison of 12 candidate models. They report that 70% of subject-specific models achieve adjusted R2 values over 60%, and that ~80% of these are 'above chance' by a permutation test. They further describe which cycle periods (ultradian, circadian, multidien) contribute per subject and include a secondary analysis with a different severity marker (top-delta).

Significance. If the reported association is real, it would extend current understanding of how seizure severity is modulated over multiple timescales and could inform future chronotherapeutic approaches. The study uses a reasonable multi-cohort dataset, applies a decomposition method that does not assume stationarity, and includes a useful secondary severity-marker analysis. The authors also appropriately avoid claiming prospective prediction. However, the headline statistical claim depends on a permutation test that conditions on the already-selected model, and the reported adjusted R2 values are in-sample fits after model selection; both issues currently overstate the strength of the evidence for the central association.

major comments (2)
  1. [Supplementary S3.4; Supplementary S3.2] The permutation test in S3.4 shuffles the seizure-duration response while keeping the band power cycles from the already-selected model fixed. Because the variable set was chosen by the model-selection procedure in S3.2 on the original data (12 candidate models, group-LASSO per model, choice by adjusted R2 and LOOCV RMSE), the null distribution does not reflect the full selection process. Under the null of no association between seizure duration and any band power cycle, the selected model's adjusted R2 is drawn from the upper tail of the selection distribution, whereas the permutation null generates adjusted R2 values from a fixed, response-independent variable set. The resulting p-values are therefore anti-conservative, and the abstract's claim that ~80% of the high-R2 models are 'above chance-level' is not supported by this test. Please rerun the permutation procedure with the response shuffled before model selection (repeating group-LASSO and the 12-model comparison), or provide a formal argument for why conditioning on the selected variables is valid.
  2. [Section 4.2; Fig. 4a; Supplementary S3.3] The reported adjusted R2 values are in-sample fits computed on the same data used for group-LASSO variable selection and model selection. They are therefore optimistically biased estimates of the population association, and the claim that '70% of the models had a higher than 60% adjusted R2' describes the training data rather than a generalizable relationship. The manuscript should either provide bias-corrected estimates (e.g., via nested cross-validation or a full permutation scheme that repeats the selection) or state explicitly in the abstract and Results that the adjusted R2 values are in-sample. The term 'predicted seizure durations' in Fig. 3b and in the Supplementary (e.g., S3.3) should be replaced with 'fitted values' to avoid implying out-of-sample accuracy.
minor comments (4)
  1. [Abstract; Fig. 4a] For precision, '70% of the models' should be reported as '14 of 19 subject-specific models', and 'around 80%' should be given with the exact count (e.g., 11 of 14) rather than a rounded percentage.
  2. [Supplementary S3.4] The manuscript states that FDR correction was not applied to permutation tests; since 19 models are tested, the reported 'above chance' proportion may include false positives due to multiple comparisons. Please report how many of the permutation p-values would remain significant after a multiplicity correction.
  3. [Code and data availability] The statement 'Code and data will be made available upon acceptance' is not reproducible; the authors should provide a link to the analysis code and processed data in a public repository at the time of submission or revision.
  4. [Throughout] Minor formatting issues include inconsistent rendering of 'adjustedR2' (should be 'adjusted R²') and inconsistent capitalization of 'P-values'; Figure 4a has some subject labels that are partially obscured and would benefit from a clearer layout.

Circularity Check

1 steps flagged · score 4.0 of 10

Permutation test conditions on the already-selected variables, so the reported 'above chance' p-values do not cover the 12-model search; the significance claim is partly an artifact of the fitting process.

  1. fitted input called prediction [Supplementary S3.4 (permutation test) with Supplementary S3.2 (model selection)]
    "We fitted 12 models per subject, each with a different selection of explanatory variables. ... After selecting a small number of explanatory variables, a linear-circular regression was performed ... We obtained adjusted R2 as a model performance metric. ... In each permutation iteration, we first randomly permuted the order of the seizures by randomly shuffling the response variable (seizure duration). Then, using the band power cycles from the selected model, we applied group-LASSO and circular-linear regression including only those variables (keeping them unchanged)."

    The permutation null distribution in S3.4 is generated from the already-selected variable set, but that variable set was chosen in S3.2 by searching over 12 candidate models and applying group-LASSO to the original seizure-duration labels. The selected set is therefore a function of the response; fixing it removes the model-selection stage from the permutation null. The observed adjusted R2 is the best or near-best among the 12 fitted models, whereas each permuted statistic is computed for a single fixed model, so the null distribution is too low and the resulting p-values are anti-conservative.

full rationale

There is no definitional circularity in the core analysis: the band-power cycles are derived from interictal iEEG, the outcome is a separate ictal measurement (seizure duration), and the regression relates phase variables to that outcome. No load-bearing self-citation chain or imported uniqueness theorem is used; the MEMD method and circular-linear regression are standard external tools. The main circularity-adjacent concern is statistical rather than definitional: the permutation test in S3.4 conditions on the variables selected in S3.2, so it does not test the whole 12-model selection procedure, and the reported p-values are conditional on a response-dependent choice. Because this invalid significance test underpins the abstract's 'above chance-level' claim, a moderate circularity score is warranted, but the biological association itself is not forced by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new theoretical entities, particles, or forces; 'cycles' are empirical features extracted from the data. The free parameters and modeling choices listed above are the main inputs the central claim depends on, and the axioms capture the key statistical and domain assumptions that could invalidate the result if false.

free parameters (4)
  • Group-LASSO tuning parameter λ = Selected by 10-fold cross-validation per model and subject
    Controls the amount of variable selection; fitted on the same data later used for adjusted R2 evaluation, contributing to in-sample optimism.
  • Slow-cycle inclusion threshold = 1/3 of total recording duration
    Author-chosen criterion to include cycles longer than one day only if at least three cycles fit in the recording; data-dependent and affects which cycles enter the models.
  • Model selection RMSE tolerance = 20 percent
    Threshold used to prefer a model with higher adjusted R2 over the minimum-RMSE model; influences which model is called 'best' and then tested.
  • Imputation noise scale = 60 percent of the standard deviation of surrounding segments
    Chosen noise level added during missing-data imputation before MEMD; affects the extracted cycles, though imputed periods are not used for the final regression.
assumptions (4)
  • domain assumption MEMD extracts mode-aligned narrow-band intrinsic mode functions from multivariate band power that correspond to meaningful cycles.
    Invoked in Section 2.3; if IMFs are artifacts of the decomposition, the instantaneous phases used in regressions are not physiologically meaningful.
  • domain assumption Seizure duration is a valid proxy for seizure severity.
    Stated in Section 1 and again in the Discussion as a limitation; if duration does not track severity, the association is not about severity modulation.
  • standard math The cosine regression model captures the relationship between log seizure duration and each cycle phase.
    Standard linear-circular regression model (Eq. 18-19); assumes a sinusoidal relationship for each cycle, which may not hold for all cycles.
  • domain assumption The permutation test in S3.4 provides a valid null distribution for the reported significance.
    The test fixes the selected variables and does not account for the prior search over 12 candidate models, so the p-values are conditional on the chosen model and may be anti-conservative.

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Pith. "Pith review of Seizure duration is associated with multiple timescales in interictal iEEG band power." pith.science (2026). https://pith.science/paper/CWH6LNGL

@misc{pith2026250417888,
  author       = {Pith},
  title        = {Pith review of: Seizure duration is associated with multiple timescales in interictal iEEG band power},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CWH6LNGL}},
  note         = {Machine review of arXiv:2504.17888}
}
abstract

Background Seizure severity can change from one seizure to the next within individual people with epilepsy. It is unclear if and how seizure severity is modulated over longer timescales. Characterising seizure severity variability over time could lead to tailored treatments. In this study, we test if continuously-recorded interictal intracranial EEG (iEEG) features encapsulate signatures of such modulations. Methods We analysed 20 subjects with iEEG recordings of at least one day. We identified cycles on timescales of hours to days embedded in long-term iEEG band power and associated them with seizure severity, which we approximated using seizure duration. In order to quantify these associations, we created linear-circular statistical models of seizure duration that incorporated different band power cycles within each subject. Findings In most subjects, seizure duration was weakly to moderately correlated with individual band power cycles. Combinations of multiple band power cycles significantly explained most of the variability in seizure duration. Specifically, we found 70% of the models had a higher than 60% adjusted $R^2$ across all subjects. From these models, around 80% were deemed to be above chance-level (p-value < 0.05) based on permutation tests. Models included cycles of ultradian, circadian and slower timescales in a subject-specific manner. Interpretation These results suggest that seizure severity, as measured by seizure duration, may be modulated over timescales of minutes to days by subject-specific cycles in interictal iEEG signal properties. These cycles likely serve as markers of seizure modulating processes. Future work can investigate biological drivers of these detected fluctuations and may inform novel treatment strategies that minimise seizure severity.

Figures

Figures reproduced from arXiv: 2504.17888 by the authors.

Figure 1
Figure 1. Quantifying band power cycles and seizure duration [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Example associations of seizure duration with phases of band power cycles [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Illustration of the final selected model for one example subject [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Selected models across subjects. (a) Barplot of the adjusted R2 as obtained from the selected model for each subject. The significance is indicated with a red asterisk as determined by a permutation test for the adjusted R2 (see Suppl. Fig. S3.6). (b) Dot plot illustra…

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Works this paper leans on

70 extracted references · 50 canonical work pages

  1. [1]

    Jouny, and Gregory K

    Pegah Afra, Christophe C. Jouny, and Gregory K. Bergey. Duration of complex partial seizures: An intracranial EEG study. Epilepsia, 49 0 (4): 0 677--684, 2008. ISSN 1528-1167. doi:10.1111/j.1528-1167.2007.01420.x. URL http://onlinelibrary.wiley.com/doi/abs/10.1111/j.1528-1167.2007.01420.x

    work page arXiv 2008

  2. [2]

    A survey of cross-validation procedures for model selection

    Sylvain Arlot and Alain Celisse. A survey of cross-validation procedures for model selection. Statistics Surveys, 4 0 (none), January 2010. ISSN 1935-7516. doi:10.1214/09-SS054. URL https://projecteuclid.org/journals/statistics-surveys/volume-4/issue-none/A-survey-of-cross-validation-procedures-for-model-selection/10.1214/09-SS054.full

    doi:10.1214/09-ss054 2010

  3. [3]

    The development of a seizure severity scale as an outcome measure in epilepsy

    Gus A Baker, David F Smith, Michael Dewey, James Morrow, Pamela M Crawford, and David W Chadwick. The development of a seizure severity scale as an outcome measure in epilepsy. Epilepsy Research, 8 0 (3): 0 245--251, April 1991. ISSN 09201211. doi:10.1016/0920-1211(91)90071-M. URL https://linkinghub.elsevier.com/retrieve/pii/092012119190071M

    work page arXiv 1991

  4. [4]

    Baker, David F

    Gus A. Baker, David F. Smith, Ann Jacoby, Julie A. Hayes, and David W. Chadwick. Liverpool Seizure Severity Scale revisited. Seizure, 7 0 (3): 0 201--205, June 1998. ISSN 10591311. doi:10.1016/S1059-1311(98)80036-8. URL https://linkinghub.elsevier.com/retrieve/pii/S1059131198800368

    work page doi:10.1016/s1059-1311(98)80036-8 1998

  5. [5]

    Baud, Jonathan K

    Maxime O. Baud, Jonathan K. Kleen, Emily A. Mirro, Jason C. Andrechak, David King-Stephens , Edward F. Chang, and Vikram R. Rao. Multi-day rhythms modulate seizure risk in epilepsy. Nature Communications, 9 0 (1): 0 1--10, January 2018. ISSN 2041-1723. doi:10.1038/s41467-017-02577-y. URL https://www.nature.com/articles/s41467-017-02577-y

    doi:10.1038/s41467-017-02577-y 2018

  6. [6]

    Baud, Antoine Ghestem, Jean-Jacques Benoliel, Christel Becker, and Christophe Bernard

    Maxime O. Baud, Antoine Ghestem, Jean-Jacques Benoliel, Christel Becker, and Christophe Bernard. Endogenous multidien rhythm of epilepsy in rats. Experimental Neurology, 315: 0 82--87, May 2019. ISSN 0014-4886. doi:10.1016/j.expneurol.2019.02.006. URL https://www.sciencedirect.com/science/article/pii/S0014488618302838

    doi:10.1016/j.expneurol.2019.02.006 2019

  7. [7]

    Bazil and Thaddeus S

    Carl W. Bazil and Thaddeus S. Walczak. Effects of Sleep and Sleep Stage on Epileptic and Nonepileptic Seizures . Epilepsia, 38 0 (1): 0 56--62, 1997. ISSN 1528-1167. doi:10.1111/j.1528-1157.1997.tb01077.x. URL https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1528-1157.1997.tb01077.x

    work page arXiv 1997

  8. [8]

    Controlling the False Discovery Rate : A Practical and Powerful Approach to Multiple Testing

    Yoav Benjamini and Yosef Hochberg. Controlling the False Discovery Rate : A Practical and Powerful Approach to Multiple Testing . Journal of the Royal Statistical Society: Series B (Methodological), 57 0 (1): 0 289--300, January 1995. ISSN 00359246. doi:10.1111/j.2517-6161.1995.tb02031.x. URL https://onlinelibrary.wiley.com/doi/10.1111/j.2517-6161.1995.tb02031.x

    arXiv 1995

Show all 70 references
  1. [9]

    Circadian/multidien Molecular Oscillations and Rhythmicity of Epilepsy ( MORE )

    Christophe Bernard. Circadian/multidien Molecular Oscillations and Rhythmicity of Epilepsy ( MORE ). Epilepsia, 62 0 (1): 0 S49--S68, 2021. ISSN 1528-1167. doi:10.1111/epi.16716. URL http://onlinelibrary.wiley.com/doi/abs/10.1111/epi.16716

    2021 doi

  2. [10]

    Seizures: About the right time to explore their mechanisms

    Christophe Bernard and Astrid Nehlig. Seizures: About the right time to explore their mechanisms. Epilepsia, 62 0 (S1): 0 S1--S1, 2021. ISSN 1528-1167. doi:10.1111/epi.16782. URL http://onlinelibrary.wiley.com/doi/abs/10.1111/epi.16782

    2021 doi

  3. [11]

    Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

    Patrick Breheny and Jian Huang. Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Statistics and Computing, 25 0 (2): 0 173--187, March 2015. ISSN 1573-1375. doi:10.1007/s11222-013-9424-2. URL https://doi.org/10.100...

    2015 doi

  4. [12]

    A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods

    PRABIR BURMAN. A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76 0 (3): 0 503--514, September 1989. ISSN 0006-3444. doi:10.1093/biomet/76.3.503. URL https://doi.org/10.1093/biomet/76.3.503

    1989 doi

  5. [13]

    Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms

    Alessio Burrello, Lukas Cavigelli, Kaspar Schindler, Luca Benini, and Abbas Rahimi. Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms . In 2019 Design , Automation & Test in Europe Conference & Exhibition ( DATE ...

    2019

  6. [14]

    Allen, Xiaosong He, Karin Trimmel, Sjoerd B

    Lorenzo Caciagli, Luke A. Allen, Xiaosong He, Karin Trimmel, Sjoerd B. Vos, Maria Centeno, Marian Galovic, Meneka K. Sidhu, Pamela J. Thompson, Danielle S. Bassett, Gavin P. Winston, John S. Duncan, Matthias J. Koepp, and Michael R. Sperling. Thalamus and focal to bilateral se...

    2020 doi

  7. [15]

    Chronotherapy in the treatment of epilepsy

    Paul Carney, David Stanley, and Sachin Talathi. Chronotherapy in the treatment of epilepsy. ChronoPhysiology and Therapy, 4: 0 109--123, November 2014. ISSN 2230-2026. doi:10.2147/CPT.S54530. URL http://www.dovepress.com/chronotherapy-in-the-treatment-of-epilepsy-peer-reviewed...

    2014 doi

  8. [16]

    Cook, Philippa J

    Mark J. Cook, Philippa J. Karoly, Dean R. Freestone, David Himes, Kent Leyde, Samuel Berkovic, Terence O'Brien, David B. Grayden, and Ray Boston. Human focal seizures are characterized by populations of fixed duration and interval. Epilepsia, 57 0 (3): 0 359--368, March 2016. ...

    2016 doi

  9. [18]

    Risti \'c , Gerald Walser, Giorgi Kuchukhidze, Iris Unterberger, Julia H \"o fler, Edda Amann, and Eugen Trinka

    Judith Dobesberger, Aleksandar J. Risti \'c , Gerald Walser, Giorgi Kuchukhidze, Iris Unterberger, Julia H \"o fler, Edda Amann, and Eugen Trinka. Duration of focal complex, secondarily generalized tonic clonic, and primarily generalized tonic clonic seizures A video- EEG anal...

    2015 doi

  10. [19]

    The Chalfont Seizure Severity Scale

    J S Duncan and J W Sander. The Chalfont Seizure Severity Scale . Journal of Neurology, Neurosurgery & Psychiatry, 54 0 (10): 0 873--876, October 1991. ISSN 0022-3050. doi:10.1136/jnnp.54.10.873. URL https://jnnp.bmj.com/lookup/doi/10.1136/jnnp.54.10.873

    1991 doi

  11. [20]

    Duy, Gregory L

    Phan Q. Duy, Gregory L. Krauss, Nathan E. Crone, Molly Ma, and Emily L. Johnson. Antiepileptic drug withdrawal and seizure severity in the epilepsy monitoring unit. Epilepsy & Behavior, 109: 0 107128, August 2020. ISSN 1525-5050. doi:10.1016/j.yebeh.2020.107128. URL https://ww...

    2020

  12. [21]

    Lipton, Matthias D \"u mpelmann, Alan D

    Victor Ferastraoaru, Andreas Schulze-Bonhage , Richard B. Lipton, Matthias D \"u mpelmann, Alan D. Legatt, Julie Blumberg, and Sheryl R. Haut. Termination of seizure clusters is related to the duration of focal seizures. Epilepsia, 57 0 (6): 0 889--895, June 2016. ISSN 1528-11...

    2016 doi

  13. [22]

    Fingelkurts and Alexander A

    Andrew A. Fingelkurts and Alexander A. Fingelkurts. Operational Architectonics of the Human Brain Biopotential Field : Towards Solving the Mind-Brain Problem . Brain and Mind, 2 0 (3): 0 261--296, December 2001. ISSN 1573-3300. doi:10.1023/A:1014427822738. URL https://doi.org/...

    2001 doi

  14. [23]

    Gascoigne, Leonard Waldmann, Gabrielle M

    Sarah J. Gascoigne, Leonard Waldmann, Gabrielle M. Schroeder, Mariella Panagiotopoulou, Jess Blickwedel, Fahmida Chowdhury, Alison Cronie, Beate Diehl, John S. Duncan, Jennifer Falconer, Ryan Faulder, Yu Guan, Veronica Leach, Shona Livingstone, Christoforos Papasavvas, Rhys H....

    2023 doi

  15. [24]

    Gliske, Zachary T

    Stephen V. Gliske, Zachary T. Irwin, Cynthia Chestek, Garnett L. Hegeman, Benjamin Brinkmann, Oren Sagher, Hugh J. L. Garton, Greg A. Worrell, and William C. Stacey. Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings. Nature...

    2018 doi

  16. [25]

    Huang, Zheng Shen, Steven R

    Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, and Henry H. Liu. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Soc...

    1971

  17. [26]

    A confidence limit for the empirical mode decomposition and Hilbert spectral analysis

    Norden E Huang, Man-Li C Wu, Steven R Long, Samuel S.P Shen, Wendong Qu, Per Gloersen, and Kuang L Fan. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engin...

    2003

  18. [27]

    Hilbert- Huang Transform and Its Applications

    Norden Eh Huang. Hilbert- Huang Transform and Its Applications . World Scientific , 2014. ISBN 978-981-4508-24-7

    2014

  19. [28]

    Ictal high-frequency activity in limbic thalamic nuclei varies with electrographic seizure-onset patterns in temporal lobe epilepsy

    Adeel Ilyas, Emilia Toth, Ganne Chaitanya, Kristen Riley, and Sandipan Pati. Ictal high-frequency activity in limbic thalamic nuclei varies with electrographic seizure-onset patterns in temporal lobe epilepsy. Clinical Neurophysiology, 137: 0 183--192, May 2022. ISSN 1388-2457...

    2022 doi

  20. [29]

    Gracely, and Michael R

    Sigmund Jenssen, Edward J. Gracely, and Michael R. Sperling. How Long Do Most Seizures Last ? A Systematic Comparison of Seizures Recorded in the Epilepsy Monitoring Unit . Epilepsia, 47 0 (9): 0 1499--1503, 2006. ISSN 1528-1167. doi:10.1111/j.1528-1167.2006.00622.x. URL https...

    2006

  21. [30]

    Epilepsy and Its Interaction With Sleep and Circadian Rhythm

    Bo Jin, Thandar Aung, Yu Geng, and Shuang Wang. Epilepsy and Its Interaction With Sleep and Circadian Rhythm . Frontiers in Neurology, 11: 0 327, May 2020. ISSN 1664-2295. doi:10.3389/fneur.2020.00327. URL https://www.frontiersin.org/article/10.3389/fneur.2020.00327/full

    2020

  22. [31]

    Jobst, Peter D

    Barbara C. Jobst, Peter D. Williamson, Timothy B. Neuschwander, Terrance M. Darcey, Vijay M. Thadani, and David W. Roberts. Secondarily Generalized Seizures in Mesial Temporal Epilepsy : Clinical Characteristics , Lateralizing Signs , and Association With Sleep Wake Cycle . Ep...

    2001

  23. [32]

    Kaplan, Andrew A

    Alexander Ya. Kaplan, Andrew A. Fingelkurts, Alexander A. Fingelkurts, Sergei V. Borisov, and Boris S. Darkhovsky. Nonstationary nature of the brain activity as revealed by EEG / MEG : Methodological , practical and conceptual challenges. Signal Processing, 85 0 (11): 0 2190--...

    2005 doi

  24. [33]

    Karoly, Dean R

    Philippa J. Karoly, Dean R. Freestone, Ray Boston, David B. Grayden, David Himes, Kent Leyde, Udaya Seneviratne, Samuel Berkovic, Terence O'Brien, and Mark J. Cook. Interictal spikes and epileptic seizures: Their relationship and underlying rhythmicity. Brain, 139 0 (4): 0 106...

    2016 doi

  25. [34]

    Karoly, Hoameng Ung, David B

    Philippa J. Karoly, Hoameng Ung, David B. Grayden, Levin Kuhlmann, Kent Leyde, Mark J. Cook, and Dean R. Freestone. The circadian profile of epilepsy improves seizure forecasting. Brain, 140 0 (8): 0 2169--2182, August 2017. ISSN 0006-8950. doi:10.1093/brain/awx173. URL https:...

    2017 doi

  26. [35]

    Circadian and circaseptan rhythms in human epilepsy: A retrospective cohort study

    Philippa J Karoly, Daniel M Goldenholz, Dean R Freestone, Robert E Moss, David B Grayden, William H Theodore, and Mark J Cook. Circadian and circaseptan rhythms in human epilepsy: A retrospective cohort study. The Lancet Neurology, 17 0 (11): 0 977--985, November 2018. ISSN 14...

    2018 doi

  27. [36]

    Karoly, Vikram R

    Philippa J. Karoly, Vikram R. Rao, Nicholas M. Gregg, Gregory A. Worrell, Christophe Bernard, Mark J. Cook, and Maxime O. Baud. Cycles in epilepsy. Nature Reviews Neurology, 17 0 (5): 0 267--284, May 2021. ISSN 1759-4766. doi:10.1038/s41582-021-00464-1. URL http://www.nature.c...

    2021 doi

  28. [37]

    P. A. Karthick, Hideaki Tanaka, Hui Ming Khoo, and Jean Gotman. Prediction of secondary generalization from a focal onset seizure in intracerebral EEG . Clinical Neurophysiology, 129 0 (5): 0 1030--1040, May 2018. ISSN 1388-2457. doi:10.1016/j.clinph.2018.02.122. URL https://w...

    2018 doi

  29. [38]

    Who seizes longest? Impact of clinical and demographic factors

    Elisabeth Kaufmann, Magdalena Seethaler, Michael Lauseker, Min Fan, Christian Vollmar, Soheyl Noachtar, and Jan R \'e mi. Who seizes longest? Impact of clinical and demographic factors. Epilepsia, 61 0 (7): 0 1376--1385, July 2020. ISSN 0013-9580, 1528-1167. doi:10.1111/epi.16...

    2020 doi

  30. [39]

    Seizure Duration Determined by Subdural Electrode Recordings in Adult Patients with Intractable Focal Epilepsy

    Daeyoung Kim, Jae-Wook Cho, Jihyun Lee, Eun Yeon Joo, Seung Chyul Hong, Seung Bong Hong, and Dae-Won Seo. Seizure Duration Determined by Subdural Electrode Recordings in Adult Patients with Intractable Focal Epilepsy . Journal of Epilepsy Research, 1 0 (2): 0 57--64, December ...

    2011 doi

  31. [40]

    Drug withdrawal in the epilepsy monitoring unit The patsalos table

    Jack Kirby, Veronica M Leach, Alice Brockington, Phillip Patsalos, Markus Reuber, and John Paul Leach. Drug withdrawal in the epilepsy monitoring unit The patsalos table. Seizure, 75: 0 75--81, February 2020. ISSN 1059-1311. doi:10.1016/j.seizure.2019.12.010. URL https://www.s...

    2020 doi

  32. [41]

    Lamberts, Athanasios Gaitatzis, Josemir W

    Robert J. Lamberts, Athanasios Gaitatzis, Josemir W. Sander, Christian E. Elger, Rainer Surges, and Roland D. Thijs. Postictal generalized EEG suppression. Neurology, 81 0 (14): 0 1252--1256, October 2013. ISSN 0028-3878. doi:10.1212/WNL.0b013e3182a6cbeb. URL https://www.ncbi....

    2013 doi

  33. [42]

    Capturing time-varying brain dynamics

    Klaus Lehnertz, Christian Geier, Thorsten Rings, and Kirsten Stahn. Capturing time-varying brain dynamics. EPJ Nonlinear Biomedical Physics, 5: 0 2, 2017. ISSN 2195-0008. doi:10.1051/epjnbp/2017001. URL http://www.epj-nbp.org/10.1051/epjnbp/2017001

    2017

  34. [43]

    Loddenkemper, M

    T. Loddenkemper, M. Vendrame, M. Zarowski, M. Gregas, A. V. Alexopoulos, E. Wyllie, and S. V. Kothare. Circadian patterns of pediatric seizures. Neurology, 76 0 (2): 0 145--153, January 2011. ISSN 1526-632X. doi:10.1212/WNL.0b013e318206ca46

    2011 doi

  35. [44]

    K. V. Mardia. Linear- Circular Correlation Coefficients and Rhythmometry . Biometrika, 63 0 (2): 0 403--405, 1976. ISSN 0006-3444. doi:10.2307/2335637. URL http://www.jstor.org/stable/2335637

    1976

  36. [45]

    Mardia and Peter E

    Kanti V. Mardia and Peter E. Jupp, editors. Directional Statistics . Wiley Series in Probability and Statistics . John Wiley & Sons, Inc. , Hoboken, NJ, USA , January 1999. ISBN 978-0-470-31697-9 978-0-471-95333-3. doi:10.1002/9780470316979. URL http://doi.wiley.com/10.1002/97...

    1999 doi

  37. [46]

    Mitsis, Maria N

    Georgios D. Mitsis, Maria N. Anastasiadou, Manolis Christodoulakis, Eleftherios S. Papathanasiou, Savvas S. Papacostas, and Avgis Hadjipapas. Functional brain networks of patients with epilepsy exhibit pronounced multiscale periodicities, which correlate with seizure onset. Hu...

    2020 doi

  38. [47]

    Manuela Ochoa-Urrea , Nuria Lacuey, Laura Vilella, Liang Zhu, Shirin Jamal-Omidi , M. R. Sandhya Rani, Johnson P. Hampson, Mojtaba Dayyani, Jaison Hampson, Norma J. Hupp, Shiqiang Tao, Rup K. Sainju, Daniel Friedman, Maromi Nei, Catherine Scott, Luke Allen, Brian K. Gehlbach, ...

    2021

  39. [48]

    Panagiotopoulou, C

    M. Panagiotopoulou, C. A. Papasavvas, G. M. Schroeder, R. H. Thomas, P. N. Taylor, and Y. Wang. Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions. Human Brain Mapping, 2022. doi:10.1002/hbm.25796. URL https...

    2022 doi

  40. [49]

    Circular statistics in R

    Giuseppe Pandolfo. Circular statistics in R . Journal of Applied Statistics, 42 0 (4): 0 918--919, April 2015. ISSN 0266-4763, 1360-0532. doi:10.1080/02664763.2014.991072. URL http://www.tandfonline.com/doi/abs/10.1080/02664763.2014.991072

    2015

  41. [50]

    Pattnaik, Nina J

    Akash R. Pattnaik, Nina J. Ghosn, Ian Z. Ong, Andrew Y. Revell, William K. S. Ojemann, Brittany H. Scheid, John M. Bernabei, Erin Conrad, Saurabh R. Sinha, Kathryn A. Davis, Nishant Sinha, and Brian Litt. A quantitative tool for seizure severity: Diagnostic and therapeutic app...

    2022 doi

  42. [51]

    Payne, Philippa J

    Daniel E. Payne, Philippa J. Karoly, Dean R. Freestone, Ray Boston, Wendyl D'Souza, Ewan Nurse, Levin Kuhlmann, Mark J. Cook, and David B. Grayden. Postictal suppression and seizure durations: A patient-specific, long-term iEEG analysis. Epilepsia, 59 0 (5): 0 1027--1036, 2018...

    2018 doi

  43. [52]

    Payne, Katrina L

    Daniel E. Payne, Katrina L. Dell, Phillipa J. Karoly, Vaclav Kremen, Vaclav Gerla, Levin Kuhlmann, Gregory A. Worrell, Mark J. Cook, David B. Grayden, and Dean R. Freestone. Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesia...

    2021 doi

  44. [53]

    Danison, and Masud Seyal

    Weifeng Peng, Jessica L. Danison, and Masud Seyal. Postictal generalized EEG suppression and respiratory dysfunction following generalized tonic clonic seizures in sleep and wakefulness. Epilepsia, 58 0 (8): 0 1409--1414, 2017. ISSN 1528-1167. doi:10.1111/epi.13805. URL http:/...

    2017 doi

  45. [54]

    Recent advances in directional statistics

    Arthur Pewsey and Eduardo Garc \'i a-Portugu \'e s . Recent advances in directional statistics. arXiv:2005.06889 [stat], September 2020. URL http://arxiv.org/abs/2005.06889

    2005 arXiv

  46. [55]

    Leguia, Thomas K

    Timoth \'e e Proix, Wilson Truccolo, Marc G. Leguia, Thomas K. Tcheng, David King-Stephens , Vikram R. Rao, and Maxime O. Baud. Forecasting seizure risk in adults with focal epilepsy: A development and validation study. The Lancet. Neurology, 20 0 (2): 0 127--135, February 202...

    2021 doi

  47. [56]

    Chronopharmacology of Anti-Convulsive Therapy

    Sriram Ramgopal, Sigride Thome-Souza , and Tobias Loddenkemper. Chronopharmacology of Anti-Convulsive Therapy . Current Neurology and Neuroscience Reports, 13 0 (4): 0 339, March 2013. ISSN 1534-6293. doi:10.1007/s11910-013-0339-2. URL https://doi.org/10.1007/s11910-013-0339-2

    2013 doi

  48. [57]

    Rehman and D

    N. Rehman and D. P. Mandic. Multivariate empirical mode decomposition. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 466 0 (2117): 0 1291--1302, May 2010. doi:10.1098/rspa.2009.0502. URL https://royalsocietypublishing.org/doi/10.1098/rspa...

    2010

  49. [58]

    Gabriel Rilling, Patrick Flandrin, Paulo Goncalves, and Jonathan M. Lilly. Bivariate Empirical Mode Decomposition . IEEE Signal Processing Letters, 14 0 (12): 0 936--939, December 2007. ISSN 1070-9908, 1558-2361. doi:10.1109/LSP.2007.904710

    2007

  50. [59]

    Schroeder, Beate Diehl, Fahmida A

    Gabrielle M. Schroeder, Beate Diehl, Fahmida A. Chowdhury, John S. Duncan, Jane de Tisi, Andrew J. Trevelyan, Rob Forsyth, Andrew Jackson, Peter N. Taylor, and Yujiang Wang. Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy. ...

    2020 doi

  51. [60]

    Multiple mechanisms shape the relationship between pathway and duration of focal seizures

    Gabrielle M Schroeder, Fahmida A Chowdhury, Mark J Cook, Beate Diehl, John S Duncan, Philippa J Karoly, Peter N Taylor, and Yujiang Wang. Multiple mechanisms shape the relationship between pathway and duration of focal seizures. Brain Communications, 4 0 (4): 0 fcac173, August...

    2022 doi

  52. [61]

    Schroeder, Philippa J

    Gabrielle M. Schroeder, Philippa J. Karoly, Matias Maturana, Peter N. Taylor, Mark J. Cook, and Yujiang Wang. Chronic iEEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states, January 2022 b . URL http://arxiv.org/abs/2201.11600

    2022 arXiv

  53. [62]

    Kalitzin, Jaime Parra, Piotr Suffczynski, and Demetrios N

    Fernando Lopes Da Silva, Wouter Blanes, Stiliyan N. Kalitzin, Jaime Parra, Piotr Suffczynski, and Demetrios N. Velis. Epilepsies as Dynamical Diseases of Brain Systems : Basic Models of the Transition Between Normal and Epileptic Activity . Epilepsia, 44 0 (s12): 0 72--83, 200...

    2003

  54. [63]

    Simeone, Kristina A

    Timothy A. Simeone, Kristina A. Simeone, Carl E. Stafstrom, and Jong M. Rho. Do ketone bodies mediate the anti-seizure effects of the ketogenic diet? Neuropharmacology, 133: 0 233--241, May 2018. ISSN 0028-3908. doi:10.1016/j.neuropharm.2018.01.011. URL https://www.sciencedire...

    2018 doi

  55. [64]

    Do seizures in patients with refractory epilepsy vary between wakefulness and sleep? Journal of Neurology, Neurosurgery, and Psychiatry, 77 0 (9): 0 1076--1078, September 2006

    S Sinha, M Brady, C A Scott, and M C Walker. Do seizures in patients with refractory epilepsy vary between wakefulness and sleep? Journal of Neurology, Neurosurgery, and Psychiatry, 77 0 (9): 0 1076--1078, September 2006. ISSN 0022-3050. doi:10.1136/jnnp.2006.088385. URL https...

    2006 arXiv

  56. [65]

    Spencer, Felice T

    David C. Spencer, Felice T. Sun, Sarah N. Brown, Barbara C. Jobst, Nathan B. Fountain, Victoria S. S. Wong, Emily A. Mirro, and Mark Quigg. Circadian and ultradian patterns of epileptiform discharges differ by seizure-onset location during long-term ambulatory intracranial mon...

    2016 doi

  57. [66]

    Spencer, Dennis D

    Susan S. Spencer, Dennis D. Spencer, P \'e t \'e r D. Williamson, and Richard H. Mattson. Ictal Effects of Anticonvulsant Medication Withdrawal in Epileptic Patients . Epilepsia, 22 0 (3): 0 297--307, 1981. ISSN 1528-1167. doi:10.1111/j.1528-1157.1981.tb04113.x. URL http://onl...

    1981

  58. [67]

    Suffczynski, F.H

    P. Suffczynski, F.H. Lopes da Silva , J. Parra, D.N. Velis, B.M. Bouwman, C.M. van Rijn , P. van Hese , P. Boon, H. Khosravani, M. Derchansky, P. Carlen, and S. Kalitzin. Dynamics of Epileptic Phenomena Determined From Statistics of Ictal Transitions . IEEE Transactions on Bio...

    2006

  59. [68]

    Todorova, Valentina S

    Koraliya S. Todorova, Valentina S. Velikova, Ara G. Kaprelyan, and Stefan T. Tsekov. SEIZURE SEVERITY AS AN ALTERNATIVE MEASURE OF OUTCOME IN EPILEPSY . Journal of IMAB - Annual Proceeding (Scientific Papers), 19 0 (3): 0 433--437, July 2013. ISSN 1312773X. doi:10.5272/jimab.2...

    2013 doi

  60. [69]

    Hourly analysis of cerebrospinal fluid glucose shows large diurnal fluctuations

    Marcel M Verbeek, Wilhelmina G Leen, Mich \`e l A Willemsen, Diane Slats, and Jurgen A Claassen. Hourly analysis of cerebrospinal fluid glucose shows large diurnal fluctuations. Journal of Cerebral Blood Flow & Metabolism, 36 0 (5): 0 899--902, May 2016. ISSN 0271-678X. doi:10...

    2016 doi

  61. [70]

    Model selection and estimation in regression with grouped variables

    Ming Yuan and Yi Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68 0 (1): 0 49--67, February 2006. ISSN 1369-7412, 1467-9868. doi:10.1111/j.1467-9868.2005.00532.x. URL http...

    2006

  62. [71]

    Influence on Ictal Seizure Semiology of Rapid Withdrawal of Carbamazepine and Valproate in Monotherapy

    Dong Zhou, Ying Wang, Peter Hopp, Frank Kerling, Annette Kirchner, Elisabeth Pauli, and Hermann Stefan. Influence on Ictal Seizure Semiology of Rapid Withdrawal of Carbamazepine and Valproate in Monotherapy . Epilepsia, 43 0 (4): 0 386--393, 2002. ISSN 1528-1167. doi:10.1046/j...

    2002

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.