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Brain-wide properties of slow waves across vigilance states.

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  1. [1] § Materials and methods › Identification of SWs ↔ iDetectSlowWave.m, lines 4–29 · score 0.51 · SW detection, candidate, zero crossings, 0.25 s, waves, 0.5 Hz

Paper

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The authors' code

MATLAB · 180 lines · 7.8 KB · MIT · 1 match

  2. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  3. % Slow wave detector
  4. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  6. % Detection of slow waves based on criteria defined in Frauscher et al.
  7. % (2015) Brain and Riedner et al. (2007) Sleep.
  8. % If used, thank you for citing:
  9. % Sheybani et al. (2023) Nat Comm (in revision)
  10. % Sheybani et al. (2023) Brain Communications
  11. % Given their substantial theorical inputs, it would be fair to cite
  12. % Frauscher et al. (2015) Brain and Riedner et al. (2007) Sleep as well
  14. % The code is shared without any warranty
  16. % Laurent Sheybani, laboratory of Prof. Matthew C Walker, University
  17. % College London (UCL), London, UK
  19. rawdata = ; % lines are electrodes, columns are timeframes
  20. n = ; % filter order
  21. sf = ; % sampling frequency
  22. fc_low = 0.5; % high-pass for SW detection
  23. fc_high = 4; % low-pass for SW detection
  24. min_duration_ZeroCrossing_sec = 0.25; % minimal duration of a half-wave, in s
  25. max_duration_ZeroCrossing_sec = 1; % maximal duration of a half-wave, in s
  26. keep_above_prctle = []; % threshold above which candidate waves are saved
  28. % For simplicity, we remove the condition "no_IED_before", and assume that
  29. % the post-processing pruning controls for that
  31. %% Filter the data for SW detection
  32. [b_low a_low] = butter(n, 2*fc_low/sf,'high'); % coefficients of the high pass-filter
  33. [b_high a_high] = butter(n, 2*fc_high/sf,'low'); % coefficients of the low pass-filter
  35. min_duration_ZeroCrossing = min_duration_ZeroCrossing_sec * sf;
  36. max_duration_ZeroCrossing = max_duration_ZeroCrossing_sec * sf;
  38. thedata = filtfilt(b_low, a_low, rawdata'); % filtering (high-pass)
  39. thedata = filtfilt(b_high, a_high, thedata)'; % filtering (low-pass)
  41. %% Look for SW of default polarity
  42. idx_ZeroCrossing = iFindZeroCrossing(thedata);
  43. clear PosToNeg_ZeroCrossing
  44. for k = 1 : length(idx_ZeroCrossing)
  45. if thedata(k,idx_ZeroCrossing{k}(1) + 1) > thedata(k,idx_ZeroCrossing{k}(1))
  46. PosToNeg_ZeroCrossing{k} = idx_ZeroCrossing{k}(2 : 2 : end);
  47. else
  48. PosToNeg_ZeroCrossing{k} = idx_ZeroCrossing{k}(1 : 2 : end);
  49. end
  50. end
  52. % Initiate variables
  53. onset_SWA = cell(1,length(PosToNeg_ZeroCrossing));
  54. offset_SWA = cell(1,length(PosToNeg_ZeroCrossing));
  55. SWA = cell(1, length(PosToNeg_ZeroCrossing));
  56. SWA_onset = cell(1, length(PosToNeg_ZeroCrossing));
  57. SWA_offset = cell(1, length(PosToNeg_ZeroCrossing));
  58. SWA_middle = cell(1, length(PosToNeg_ZeroCrossing));
  60. % Across electrodes
  61. disp('Negative waves...')
  62. clear diff_ZeroCrossing
  63. for k = 1 : length(PosToNeg_ZeroCrossing)
  64. if isempty(PosToNeg_ZeroCrossing{k})
  65. SWA_onset{k} = [];
  66. SWA_offset{k} = [];
  67. SWA_middle{k} = [];
  68. SWA{k} = [];
  69. else
  70. diff_ZeroCrossing = diff(idx_ZeroCrossing{k});
  71. if thedata(k,idx_ZeroCrossing{k}(1) + 1) > thedata(k,idx_ZeroCrossing{k}(1))
  72. PosToNeg_diff_ZeroCrossing = diff_ZeroCrossing(2 : 2 : end);
  73. else
  74. PosToNeg_diff_ZeroCrossing = diff_ZeroCrossing(1 : 2 : end);
  75. end
  76. onset_SWA{k} = PosToNeg_ZeroCrossing{k}(PosToNeg_diff_ZeroCrossing > min_duration_ZeroCrossing...
  77. & PosToNeg_diff_ZeroCrossing < max_duration_ZeroCrossing);
  78. for m = 1 : length(onset_SWA{k})
  79. offset_SWA{k} = [offset_SWA{k} idx_ZeroCrossing{k}(find(onset_SWA{k}(m) == idx_ZeroCrossing{k}) + 1)];
  80. end
  82. for m = 1 : length(onset_SWA{k})
  83. amp_temp(m) = max(abs(thedata(k,onset_SWA{k}(m) : offset_SWA{k}(m))));
  84. end
  85. threshold_amp = prctile(amp_temp, keep_above_prctle);
  86. clear amp_temp
  88. m_trace = 1;
  89. for m = 1 : length(onset_SWA{k})
  91. if max(abs(thedata(k, onset_SWA{k}(m) : offset_SWA{k}(m)))) >= max(abs(threshold_amp))
  92. [maxi idx] = max(abs(thedata(k, onset_SWA{k}(m) : offset_SWA{k}(m))));
  93. if onset_SWA{k}(m) + idx - (3*sf) > 0 && onset_SWA{k}(m) + idx + (3*sf) < length(thedata(k,:))
  94. SWA{k}(m_trace,:) = rawdata(k, onset_SWA{k}(m) + idx + [(-3*sf) : (3*sf)]);
  95. SWA_onset{k}(m_trace) = onset_SWA{k}(m);
  96. SWA_offset{k}(m_trace) = offset_SWA{k}(m);
  97. SWA_middle{k}(m_trace) = onset_SWA{k}(m) + idx;
  98. m_trace = m_trace + 1;
  99. end
  100. end
  101. end
  102. clear threshold_amp
  103. end
  104. end
  106. %% Look for SW of opposite polarity
  107. clear rawdata_inv
  108. for k = 1 : size(rawdata,1)
  109. rawdata_inv(k,:) = rawdata(k,:);
  110. end
  111. thedata_inv = filtfilt(b_low, a_low, rawdata_inv'); % filtering (high-pass)
  112. thedata_inv = filtfilt(b_high, a_high, thedata_inv)'; % filtering (low-pass)
  114. idx_ZeroCrossing_inv = iFindZeroCrossing(thedata_inv);
  115. clear NegToPos_ZeroCrossing_inv
  116. for k = 1 : length(idx_ZeroCrossing_inv)
  117. if thedata_inv(k,idx_ZeroCrossing_inv{k}(1) + 1) < thedata_inv(k,idx_ZeroCrossing_inv{k}(1))
  118. NegToPos_ZeroCrossing_inv{k} = idx_ZeroCrossing_inv{k}(2 : 2 : end);
  119. else
  120. NegToPos_ZeroCrossing_inv{k} = idx_ZeroCrossing_inv{k}(1 : 2 : end);
  121. end
  122. end
  124. % Initiate variables
  125. onset_SWA_inv = cell(1,length(NegToPos_ZeroCrossing_inv));
  126. offset_SWA_inv = cell(1,length(NegToPos_ZeroCrossing_inv));
  127. SWA_inv = cell(1, length(NegToPos_ZeroCrossing_inv));
  128. SWA_inv_onset = cell(1, length(NegToPos_ZeroCrossing_inv));
  129. SWA_inv_offset = cell(1, length(NegToPos_ZeroCrossing_inv));
  130. SWA_inv_middle = cell(1, length(NegToPos_ZeroCrossing_inv));
  132. % Across electrodes
  133. disp('Positive waves...')
  134. clear diff_ZeroCrossing_inv
  135. for k = 1 : length(NegToPos_ZeroCrossing_inv)
  136. if isempty(NegToPos_ZeroCrossing_inv{k})
  137. SWA_inv_onset{k} = [];
  138. SWA_inv_offset{k} = [];
  139. SWA_inv_middle{k} = [];
  140. SWA_inv{k} = [];
  141. else
  142. diff_ZeroCrossing_inv = diff(idx_ZeroCrossing_inv{k});
  143. if thedata_inv(k,idx_ZeroCrossing_inv{k}(1) + 1) < thedata_inv(k,idx_ZeroCrossing_inv{k}(1))
  144. NegToPos_diff_ZeroCrossing_inv = diff_ZeroCrossing_inv(2 : 2 : end);
  145. else
  146. NegToPos_diff_ZeroCrossing_inv = diff_ZeroCrossing_inv(1 : 2 : end);
  147. end
  148. onset_SWA_inv{k} = NegToPos_ZeroCrossing_inv{k}(NegToPos_diff_ZeroCrossing_inv > min_duration_ZeroCrossing...
  149. & NegToPos_diff_ZeroCrossing_inv < max_duration_ZeroCrossing);
  150. for m = 1 : length(onset_SWA_inv{k})
  151. offset_SWA_inv{k} = [offset_SWA_inv{k} idx_ZeroCrossing_inv{k}(find(onset_SWA_inv{k}(m) == idx_ZeroCrossing_inv{k}) + 1)];
  152. end
  154. for m = 1 : length(onset_SWA_inv{k})
  155. amp_temp_inv(m) = max(abs(thedata_inv(k,onset_SWA_inv{k}(m) : offset_SWA_inv{k}(m))));
  156. end
  157. threshold_amp_inv = prctile(amp_temp_inv, current_threshold);
  158. clear amp_temp_inv
  160. m_trace = 1;
  161. for m = 1 : length(onset_SWA_inv{k})
  163. if max(abs(thedata_inv(k, onset_SWA_inv{k}(m) : offset_SWA_inv{k}(m)))) >= max(abs(threshold_amp_inv))
  164. [maxi idx] = max(abs(thedata_inv(k, onset_SWA_inv{k}(m) : offset_SWA_inv{k}(m))));
  165. if onset_SWA_inv{k}(m) + idx - (3*sf) > 0 && onset_SWA_inv{k}(m) + idx + (3*sf) < length(thedata_inv(k,:))
  166. SWA_inv{k}(m_trace,:) = rawdata_inv(k, onset_SWA_inv{k}(m) + idx + [(-3*sf) : (3*sf)]);
  167. SWA_inv_onset{k}(m_trace) = onset_SWA_inv{k}(m);
  168. SWA_inv_offset{k}(m_trace) = offset_SWA_inv{k}(m);
  169. SWA_inv_middle{k}(m_trace) = onset_SWA_inv{k}(m) + idx;
  170. m_trace = m_trace + 1;
  171. end
  172. end
  173. end
  174. clear threshold_amp_inv
  175. end
  176. end

iDetectSlowWave.m at commit dd32d80, under MIT · at the source

Overview

Authors: Senyu Yang1, Olivia Poole2, Matthew C Walker1,3,4, Laurent Sheybani1,3,4
ORCID iDs: Senyu Yang, Matthew C Walker, Laurent Sheybani
  1. Research Department of Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
  2. Department of Epilepsy, National Hospital for Neurology and Neurosurgery, London, United Kingdom
  3. National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, United Kingdom
  4. NIHR University College London Hospitals Biomedical Research Centre, London, United Kingdom
Institutions: UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); National Hospital for Neurology and Neurosurgery (United Kingdom); University College London Hospitals NHS Foundation Trust (United Kingdom); UCL Biomedical Research Centre (United Kingdom)
Journal: Sleep advances : a journal of the Sleep Research Society, volume 7, issue 3, article zpag065
Dates: received 10 March 2026; accepted 6 June 2026; published online 22 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/sleepadvances/zpag065 · PMID 42494975 · PMCID PMC13395241 · OpenAlex W7165538059
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), epilepsy (population), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing
Keywords: slow waves (SWs), intracranial EEG, sleep–wake cycle, high-gamma (HG) amplitude, neuronal synchrony, epilepsy
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Swiss National Science Foundation (233452)
Citations: not cited yet (Europe PMC); 45 references in the paper

Abstract

Until recently, slow waves (SWs) were considered to be highly specific, if not exclusive, to sleep and non-rapid eye movement (NREM) sleep in particular. During NREM sleep, they are proposed to track and contribute to normalization of homeostatic sleep pressure. However, recent evidence has identified typical SWs during wakefulness and rapid-eye movement (REM) sleep. SWs during wakefulness have been regarded as intrusions of sleep, supported by the finding of an associated down-state of neural activity. Although this suggests that the underlying neurobiology of SWs might be shared across vigilance states, i.e. the activity of neurons is comparable, it does not address the question as to whether SWs display state-dependent differences that could reflect a homeostatic regulation. To address this question, we utilized an intracranial dataset of 106 adult patients with drug-resistant epilepsy and computed specific features of SWs—their incidence, slope, transition frequency, associated high gamma activity, multipeak morphology and overlap across brain regions. Overall, we found that changes in these features reflect a state-dependent modulation, potentially in line with expected changes in homeostatic pressure. The multipeak morphology displayed the greatest changes across states. SW differences were sufficiently specific to vigilance state that we could successfully classify these states using SW properties. Our work provides further evidence that SWs during wakefulness and REM sleep are consistent with intrusion of NREM-SW and establish normative values for future studies on SWs across vigilance states and brain regions.

Statement of Significance: Using a large dataset of intracranial recordings in 106 patients with drug-resistant epilepsy, we show that key morphological features of SWs change across vigilance states. Furthermore, we were able to successfully classify vigilance states based on these morphological properties, establishing their specificity for wakefulness, non-rapid eye movement (NREM) and rapid eye movement (REM) sleep respectively. Our work contributes to the hypothesis that SWs outsides NREM can be regarded as intrusions of NREM-SW and provides normative scaling laws for future studies on this neurophysiological entity.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

bushlab-ucl/slowWaveDetection

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: dd32d8034421413342b7b195c6e60880b6270e1b, 30 November 2023
Languages: MATLAB (3)
Size: 5 files, 3 scripts
Software Heritage: not archived
Found in: the text, “Identification of SWs”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/bushlab-ucl/slowWaveDetection

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Data are available on the Loris repository: https://mni-open-ieegatlas.research.mcgill.ca/

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 1 funder, 40 references.

Cite

This paper

Yang, S., Poole, O., Walker, M. C., & Sheybani, L. (2026). Brain-wide properties of slow waves across vigilance states. Sleep advances : a journal of the Sleep Research Society, 7(3), zpag065. https://doi.org/10.1093/sleepadvances/zpag065

BibTeX

@article{yang2026brain,
author = {Yang, Senyu and Poole, Olivia and Walker, Matthew C and Sheybani, Laurent},
title = {{Brain-wide properties of slow waves across vigilance states}},
journal = {Sleep advances : a journal of the Sleep Research Society},
year = {2026},
month = jun,
volume = {7},
number = {3},
pages = {zpag065},
publisher = {Oxford University Press},
issn = {2632-5012},
doi = {10.1093/sleepadvances/zpag065},
url = {https://doi.org/10.1093/sleepadvances/zpag065},
pmid = {42494975},
pmcid = {PMC13395241}
}

RIS

TY - JOUR
AU - Yang, Senyu
AU - Poole, Olivia
AU - Walker, Matthew C
AU - Sheybani, Laurent
TI - Brain-wide properties of slow waves across vigilance states
T2 - Sleep advances : a journal of the Sleep Research Society
J2 - Sleep Adv
PY - 2026
DA - 2026/06/22
VL - 7
IS - 3
SP - zpag065
SN - 2632-5012
PB - Oxford University Press
DO - 10.1093/sleepadvances/zpag065
UR - https://doi.org/10.1093/sleepadvances/zpag065
LA - en
ER -

CSL-JSON

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