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An automated EEG–ECG framework for CAP–HRV analysis: enabling brain–heart coupling studies in sleep disorders

Code ↔ Paper

8 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 8 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › ECG preprocessing and HRV feature extraction ↔ worked_example.m, lines 1 to 72 · score 0.81 · SampEn, mhrv toolbox directly, PhysioZoo, RMSSD, AVNN, HF · Report a mismatch
  2. [2] § Materials and methods › Control reference cohort and A1-event HRV ↔ validate_CAP_A1.m, lines 1 to 100 · score 0.78 · CAP Sleep Database, expert annotated, CAP A1 events, n16, capslpdb, channel · Report a mismatch
  3. [3] § Materials and methods › Control reference cohort and A1-event HRV ↔ Shaween_HRV_Features_w_CAP.m, lines 1 to 35 · score 0.76 · n1 n16, CAP Sleep Database, Pan Tompkins, HRV feature, CAP A1, healthy · Report a mismatch
  4. [4] § Materials and methods › CAP A1 detection (rule-based implementation) ↔ validate_CAP_A1.m, lines 1 to 100 · score 0.72 · expert annotated, CAP A1 events, validated, Cohen, sensitivity, module · Report a mismatch
  5. [5] § Materials and methods › EEG preprocessing ↔ validate_CAP_A1.m, lines 113 to 127 · score 0.71 · 0.5–4 Hz, delta band, morphology, CAP A1, channel, bandpass · Report a mismatch
  6. [6] § Materials and methods › ECG preprocessing and HRV feature extraction ↔ worked_example.m, lines 1 to 72 · score 0.66 · Pan Tompkins, peak detection, ECG epochs, fallback, SDNN, bpm · Report a mismatch
  7. [7] § Materials and methods › ECG preprocessing and HRV feature extraction ↔ ShaweenHRV_mhrv.m, the whole file · a weak match · score 0.65 · Pan Tompkins, peak detection, ECG epochs, fallback, intervals, nonlinear · Report a mismatch
  8. [8] § Materials and methods › ECG preprocessing and HRV feature extraction ↔ ShaweenHRV_mhrv.m, the whole file · a weak match · score 0.61 · mhrv toolbox directly, PhysioZoo, power, DFA, HRV features, Lomb · Report a mismatch

Paper

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The authors' code

MATLAB · 186 lines · 7.6 KB · CC-BY-4.0 · 3 matches

  1. function results = validate_CAP_A1(dbDir, records, outCsv)
  2. % VALIDATE_CAP_A1 Benchmark YOUR rule-based CAP A1 detector against the
  3. % expert annotations of the PhysioNet CAP Sleep Database (capslpdb v1.0.0).
  4. %
  5. % Addresses Reviewer 1, Comment 1 (and the Editor note): reports sensitivity,
  6. % specificity, PPV, F1, and Cohen's kappa for CAP A1 detection vs. expert
  7. % scoring, at both 1-second and event levels, restricted to SWS (S3/S4).
  8. %
  9. % This version uses PhysioNet's own ScoringReader.m (shipped with the
  10. % database), so you do NOT parse the .txt files yourself.
  11. %
  12. % USAGE
  13. % r = validate_CAP_A1('capslpdb/', {'n1','n2','sdb1','sdb2'}, 'cap_a1_val.csv')
  14. % r = validate_CAP_A1('capslpdb/', [], 'cap_a1_val.csv') % uses RECORDS file
  15. %
  16. % INPUTS
  17. % dbDir folder holding the downloaded capslpdb files (*.edf, *.txt,
  18. % ScoringReader.m, RECORDS). Put this .m file there or add to path.
  19. % records cell array of record names WITHOUT extension, e.g. {'n1','sdb1'}.
  20. % If empty, all names in the RECORDS file are used.
  21. % RECOMMENDED for a CAP-A1/SWS benchmark: the 16 controls
  22. % {'n1'..'n16'} (clean SWS) plus the 4 SDB records {'sdb1'..'sdb4'}.
  23. % outCsv path for the per-record results table.
  24. %
  25. % >>> THE ONLY LINE YOU MUST EDIT is marked "WIRE HERE": call your detector. <<<
  26. % Your detector must return Nx2 [onset_sec offset_sec] of detected A1 events.
  27. %
  28. % REQUIREMENTS
  29. % - MATLAB R2023b+ (edfread/edfinfo), or replace local_load_eeg with your reader
  30. % - ScoringReader.m from the database (on the MATLAB path)
  31. % - Your CAP A1 module
  32. %
  33. % Author: prepared for the Frontiers in Physiology revision. CC-BY 4.0.
  35. if nargin < 3 || isempty(outCsv), outCsv = 'cap_a1_validation.csv'; end
  36. if nargin < 2 || isempty(records)
  37. records = local_read_records(fullfile(dbDir,'RECORDS'));
  38. end
  39. assert(exist('ScoringReader','file')==2, ...
  40. 'ScoringReader.m not found on path. Add the capslpdb folder to the MATLAB path.');
  42. rows = {}; allTP=0; allFP=0; allTN=0; allFN=0;
  44. for i = 1:numel(records)
  45. rec = records{i};
  46. txt = fullfile(dbDir,[rec '.txt']);
  47. edf = fullfile(dbDir,[rec '.edf']);
  48. if ~isfile(txt) || ~isfile(edf)
  49. warning('Missing files for %s, skipping.', rec); continue;
  50. end
  52. % ---- 1. Expert annotations via PhysioNet's ScoringReader.m -----------
  53. % ScoringReader returns the macrostructure hypnogram (0=W,1..4=S1..S4,5=REM)
  54. % and three arrays for the CAP A phases: start time (s), duration (s),
  55. % subtype (1=A1, 2=A2, 3=A3). Adjust the output order if your copy differs.
  56. [hypno, capStart, capDur, capType] = ScoringReader(txt); % see note above
  57. hypno = hypno(:);
  58. isA1 = (capType(:) == 1);
  59. a1_exp = [capStart(isA1), capStart(isA1)+capDur(isA1)]; % Nx2 sec
  61. % ---- 2. Load the central EEG derivation -----------------------------
  62. [eeg, fs, chan] = local_load_eeg(edf);
  64. % ---- 3. Run YOUR detector (WIRE HERE) -------------------------------
  65. caps_det = detect_cap_a1(eeg, fs, hypno); % <<< WIRE HERE: your A1 module
  67. % ---- 4. Build 1-second SWS masks (gold vs detected) -----------------
  68. T = max([numel(hypno)*30, ceil(max([a1_exp(:);0])), ...
  69. ceil(max([caps_det(:);0]))]);
  70. sws = local_stage_mask(hypno, T, [3 4]); % S3/S4 only
  71. gold = local_events_to_mask(a1_exp, T) & sws;
  72. pred = local_events_to_mask(caps_det,T) & sws;
  74. TP=sum(pred&gold); FP=sum(pred&~gold); FN=sum(~pred&gold); TN=sum(~pred&~gold);
  75. [se,sp,f1,kap,ppv] = local_metrics(TP,FP,TN,FN);
  76. [evSe,evPpv,evF1] = local_event_match(caps_det, a1_exp, sws, T, 0.5);
  78. rows(end+1,:) = {rec, char(chan), sum(isA1 & local_onset_in_sws(capStart,hypno,[3 4])),...
  79. size(caps_det,1), se, sp, ppv, f1, kap, evSe, evPpv, evF1}; %#ok<AGROW>
  80. allTP=allTP+TP; allFP=allFP+FP; allTN=allTN+TN; allFN=allFN+FN;
  81. fprintf('%-8s [%s] Se=%.3f Sp=%.3f F1=%.3f kappa=%.3f (eventF1=%.3f)\n',...
  82. rec, chan, se, sp, f1, kap, evF1);
  83. end
  85. [se,sp,f1,kap,ppv] = local_metrics(allTP,allFP,allTN,allFN);
  86. fprintf('\nPOOLED (1-s, SWS): Se=%.3f Sp=%.3f PPV=%.3f F1=%.3f Cohen kappa=%.3f\n',...
  87. se,sp,ppv,f1,kap);
  89. V = cell2table(rows,'VariableNames',{'record','channel','n_expert_A1_SWS',...
  90. 'n_detected','sensitivity','specificity','PPV','F1','kappa',...
  91. 'event_sensitivity','event_PPV','event_F1'});
  92. writetable(V,outCsv);
  93. results = struct('perRecord',V,'pooled',...
  94. struct('Se',se,'Sp',sp,'PPV',ppv,'F1',f1,'kappa',kap));
  95. fprintf('Per-record results written to %s\n', outCsv);
  96. fprintf(['\nReport in the manuscript (Section 2.4 / 3): pooled Se, Sp, F1 and ' ...
  97. 'Cohen''s kappa above, plus the per-record table as supplementary.\n']);
  98. end
  100. % ============================ helpers ===================================
  101. function recs = local_read_records(f)
  102. recs = {};
  103. if isfile(f)
  104. fid=fopen(f); c=textscan(fid,'%s'); fclose(fid); recs=c{1};
  105. else
  106. % default: controls + SDB
  107. recs = [arrayfun(@(k)sprintf('n%d',k),1:16,'uni',0), ...
  108. arrayfun(@(k)sprintf('sdb%d',k),1:4,'uni',0)];
  109. warning('RECORDS file not found; defaulting to controls n1-n16 + sdb1-4.');
  110. end
  111. end
  113. function [eeg, fs, chan] = local_load_eeg(edfPath)
  114. info = edfinfo(edfPath);
  115. labels = string(info.SignalLabels);
  116. pick = find(contains(labels,["C4-A1","C3-A2","C4A1","C3A2"],'IgnoreCase',true),1);
  117. if isempty(pick), pick = find(contains(labels,["C4","C3"],'IgnoreCase',true),1); end
  118. if isempty(pick), pick = find(contains(labels,"EEG",'IgnoreCase',true),1); end
  119. if isempty(pick), pick = 1; end
  120. chan = labels(pick);
  121. fs = double(info.NumSamples(pick))/seconds(info.DataRecordDuration);
  122. tt = edfread(edfPath,'SelectedSignals',cellstr(chan));
  123. eeg = cell2mat(tt.(matlab.lang.makeValidName(chan)));
  124. eeg = double(eeg(:));
  125. [b,a]= butter(4,[0.5 4]/(fs/2),'bandpass'); % delta band, matches A1 morphology
  126. eeg = filtfilt(b,a,eeg);
  127. end
  129. function m = local_events_to_mask(ev, T)
  130. m = false(1,T);
  131. for k=1:size(ev,1)
  132. a=max(1,floor(ev(k,1))+1); b=min(T,ceil(ev(k,2)));
  133. if b>=a, m(a:b)=true; end
  134. end
  135. end
  137. function msk = local_stage_mask(hypno, T, stages)
  138. msk = false(1,T);
  139. for e=1:numel(hypno)
  140. if any(hypno(e)==stages)
  141. a=(e-1)*30+1; b=min(T,e*30); msk(a:b)=true;
  142. end
  143. end
  144. if ~any(msk), msk=true(1,T); end % if hypnogram empty, do not gate
  145. end
  147. function tf = local_onset_in_sws(starts, hypno, stages)
  148. tf = false(numel(starts),1);
  149. for k=1:numel(starts)
  150. e=floor(starts(k)/30)+1;
  151. if e>=1 && e<=numel(hypno) && any(hypno(e)==stages), tf(k)=true; end
  152. end
  153. end
  155. function [se,sp,f1,kap,ppv] = local_metrics(TP,FP,TN,FN)
  156. se =TP/max(TP+FN,1); sp =TN/max(TN+FP,1); ppv=TP/max(TP+FP,1);
  157. f1 =2*ppv*se/max(ppv+se,eps);
  158. N=TP+FP+TN+FN; po=(TP+TN)/max(N,1);
  159. pe=((TP+FP)*(TP+FN)+(TN+FN)*(TN+FP))/max(N^2,1);
  160. kap=(po-pe)/max(1-pe,eps);
  161. end
  163. function [se,ppv,f1] = local_event_match(det, exp, sws, T, ovl)
  164. det=local_restrict(det,sws,T); exp=local_restrict(exp,sws,T);
  165. mE=false(size(exp,1),1);
  166. for i=1:size(det,1)
  167. for j=1:size(exp,1)
  168. if mE(j), continue; end
  169. o=min(det(i,2),exp(j,2))-max(det(i,1),exp(j,1));
  170. if o<=0, continue; end
  171. shorter=min(diff(det(i,:)),diff(exp(j,:)));
  172. if o/max(shorter,eps)>=ovl, mE(j)=true; break; end
  173. end
  174. end
  175. TP=sum(mE); se=TP/max(size(exp,1),1); ppv=TP/max(size(det,1),1);
  176. f1=2*ppv*se/max(ppv+se,eps);
  177. end
  179. function ev = local_restrict(ev, sws, T)
  180. keep=false(size(ev,1),1);
  181. for k=1:size(ev,1)
  182. a=max(1,floor(ev(k,1))+1); b=min(T,ceil(ev(k,2)));
  183. if b>=a && any(sws(a:b)), keep(k)=true; end
  184. end
  185. ev=ev(keep,:);
  186. end

validate_CAP_A1.m at commit 49885ef, under CC-BY-4.0 · at the source

Overview

Authors: Shaween Shukir1, Mostafa Moussa1, Yahya AlZaabi1, Ahsan Khandoker1, Zbigniew R Struzik2
  1. Healthcare Engineering Innovation Group (HEIG), Department of Biomedical Engineering and Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates
  2. Graduate School of Education, University of Tokyo, Bunkyo-ku, Tokyo, Japan
Journal: Frontiers in physiology, volume 17, article 1864664
Dates: received 24 April 2026; accepted 7 September 2026; published online 16 October 2026
Type: Methods article · Language: English
License: CC BY
Identifiers: DOI · PMCID PMC13719611
Status: code verified (how the code is found and verified)
Categories: EEG (modality), other (modality), depression (population), sleep disorders (population), methods / tools (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Complexity, Evoked potentials, Physiology & signal measures
Keywords: cyclic alternating pattern (CAP), ECG, heart rate variability (HRV), major depressive disorder, sleep, obstructive sleep apnea
Citations: not cited yet (Europe PMC); 29 references in the paper

Abstract

Background: Multimodal sleep analysis typically requires separate processing pipelines for electroencephalography (EEG) and electrocardiography (ECG), which complicates joint investigation of sleep microstructure and autonomic regulation. Cyclic alternating pattern (CAP) scoring and heart rate variability (HRV) analysis are routinely performed in different software environments, limiting temporal alignment, reproducibility, and scalability.

Methods: We present an open-source MATLAB framework that integrates rule-based CAP A1 detection and HRV feature extraction within a single, time-synchronized workflow. The pipeline performs standardized EEG and ECG preprocessing, rule-based CAP A1 detection during slow-wave sleep (SWS) implementing the Terzano scoring criteria, R-peak detection via the Pan–Tompkins algorithm, and computation of time-domain, frequency-domain, and nonlinear HRV features by calling the open-source PhysioZoo/mhrv toolbox directly. CAP annotations are aligned with 5-minute ECG epochs through the shared polysomnography (PSG) time base. The complete code is released as an open-source repository to support reuse and extension.

Results: Because HRV features are computed by calling the PhysioZoo/mhrv toolbox directly, the pipeline introduces no reimplemented HRV routine and inherits that toolbox’s validated feature definitions. CAP A1 detection is provided as a rule-based implementation of published criteria and has not yet been benchmarked against expert manual scoring. A worked example on the OSAD+ subgroup (n = 18) demonstrates pipeline output across CAP and non-CAP epochs, and a healthy-sleep A1-event HRV reference is reported from the public CAP Sleep Database (15 controls, 785 A1 epochs) as a descriptive cross-database reference.

Conclusion: The framework provides a reproducible, rule-based, and modular tool for integrated EEG–ECG analysis of sleep microstructure and autonomic dynamics. By enabling synchronized CAP–HRV feature extraction in a single environment, it lowers the engineering barrier to brain–heart coupling research during sleep and provides a foundation for future extensions, including validated CAP detection, a matched-control study, a Python port, and cloud-based deployment.

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 8 matches between paragraphs and lines of code.

ahsankhandoker/CAP-HRV

License: CC-BY-4.0
State: the link answers, verified on 31 October 2026
Evidence: files inventoried
Commit: 49885ef1f0ea7ca741dc6bc76c247c839cd46730, 16 October 2026
Languages: MATLAB (5)
Size: 9 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability statement”
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 31 October 2026: the link answers
  • 31 October 2026: the link answers
7 files
At the source: github.com/ahsankhandoker/CAP-HRV

The paper's code and data availability statement is in the Data section.

Tracing map

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

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Data

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

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. The pipeline source code and demo dataset are available at https://github.com/ahsankhandoker/CAP-HRV and the control data are the public CAP Sleep Database (capslpdb) on PhysioNet.

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

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Version 1, 31 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 28 references.

Cite

This paper

Shukir, S., Moussa, M., AlZaabi, Y., Khandoker, A., & Struzik, Z. R. (2026). An automated EEG–ECG framework for CAP–HRV analysis: enabling brain–heart coupling studies in sleep disorders. Frontiers in physiology, 17, 1864664.

BibTeX

@article{shukir2026automated,
author = {Shukir, Shaween and Moussa, Mostafa and AlZaabi, Yahya and Khandoker, Ahsan and Struzik, Zbigniew R},
title = {{An automated EEG–ECG framework for CAP–HRV analysis: enabling brain–heart coupling studies in sleep disorders}},
journal = {Frontiers in physiology},
year = {2026},
month = oct,
volume = {17},
pages = {1864664},
publisher = {Frontiers Media SA},
issn = {1664-042X},
pmcid = {PMC13719611}
}

RIS

TY - JOUR
AU - Shukir, Shaween
AU - Moussa, Mostafa
AU - AlZaabi, Yahya
AU - Khandoker, Ahsan
AU - Struzik, Zbigniew R
TI - An automated EEG–ECG framework for CAP–HRV analysis: enabling brain–heart coupling studies in sleep disorders
T2 - Frontiers in physiology
J2 - Front Physiol
PY - 2026
DA - 2026/10/31
VL - 17
SP - 1864664
SN - 1664-042X
PB - Frontiers Media SA
LA - en
ER -

CSL-JSON

{
"id": "pmcid:PMC13719611",
"type": "article-journal",
"title": "An automated EEG–ECG framework for CAP–HRV analysis: enabling brain–heart coupling studies in sleep disorders",
"container-title": "Frontiers in physiology",
"author": [
{
"family": "Shukir",
"given": "Shaween"
},
{
"family": "Moussa",
"given": "Mostafa"
},
{
"family": "AlZaabi",
"given": "Yahya"
},
{
"family": "Khandoker",
"given": "Ahsan"
},
{
"family": "Struzik",
"given": "Zbigniew R"
}
],
"container-title-short": "Front Physiol",
"volume": "17",
"page": "1864664",
"PMCID": "PMC13719611",
"ISSN": "1664-042X",
"publisher": "Frontiers Media SA",
"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
31
]
]
}
}

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