A dataset of EEG and ECG recordings around the onset of NREM sleep from infancy to adolescence.

Code ↔ Paper

9 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 9 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Methods › Data acquisition ↔ subfunctions/read_into_montage.m, the whole file · a weak match · score 0.86 · double banana, bipolar montage, channel, EEG
[2] § Technical Validation ↔ quality_assesssment_for_github.m, lines 98–177 · score 0.78 · burst sharpness, post N2 onset, relative alpha, pre N2 onset, Spearman, centile
[3] § Data Records ↔ quality_assesssment_for_github.m, lines 1–24 · score 0.73 · anon_codex_age_sex.csv, quantized age, quantized sex, female
[4] § Technical Validation ↔ subfunctions/single_channel_features.m, lines 1–52 · score 0.73 · relative spectral power, spectral entropy, modified, PSD, duration, vector
[5] § Technical Validation ↔ quality_assesssment_for_github.m, lines 613–685 · score 0.73 · minute epoch, Pan Tompkins, ECG features, notch, quality, median
[6] § Data Records ↔ anonymize_dataset_for_github.m, lines 116–167 · score 0.63 · anon codex, quantized sex, quantized age, EDF
[7] § Technical Validation ↔ quality_assesssment_for_github.m, lines 255–303 · score 0.63 · post N2 onset, pre N2 onset, predicted age, kernel, correlation
[8] § Technical Validation ↔ quality_assesssment_for_github.m, lines 255–303 · score 0.63 · post N2 onset, pre N2 onset, predicted age, kernel, correlation
[9] § Data Records ↔ subfunctions/read_into_montage.m, the whole file · a weak match · score 0.62 · referential montage, microvolts, EDF, channels, EEG

Paper

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The authors' code

MATLAB · 897 lines · 31 KB · MIT · 5 matches

% Quality Assessment Open EEG Data
% READ IN CSV file with filenames, quantized age and sex
filename = 'anon_codex_age_sex.csv';
% Specify range and delimiter
dataLines = [2, Inf];
opts = delimitedTextImportOptions("NumVariables", 3);
opts.DataLines = dataLines;
opts.Delimiter = ",";
opts.VariableNames = ["AnonFilename", "QuantizedAgeyears", "QuantizedSex"];
opts.VariableTypes = ["string", "double", "categorical"];
opts.ExtraColumnsRule = "ignore";
opts.EmptyLineRule = "read";
opts = setvaropts(opts, "AnonFilename", "WhitespaceRule", "preserve");
opts = setvaropts(opts, ["AnonFilename", "QuantizedSex"], "EmptyFieldRule", "auto");
% Import the data
anoncodex = readtable(filename, opts);
fnames = cellstr(anoncodex{:,1});
ages = anoncodex{:,2};
sex = anoncodex{:,3};
s1 = contains(sex, 'M'); % M - Male
sex = s1;
%s2 = contains(sex, 'F'); % F - Female
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% EEG ANALYSIS
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PERFORM BASIC EVALUATIONS ON DATASET
fs1 = 250;
val1a = zeros(1, length(fnames)); val1b = val1a; val2 = val1a;
for ii = 1:length(fnames)
[dat, hdr, label, fs, scle, offs] = read_edf(fnames{ii});
data_bp_mont = read_into_montage(dat, label, scle, fs, fs1, 0);
anno = per_ha_art(data_bp_mont, fs1);
val2(ii) = sum(sum(anno))/prod(size(anno))*100;
[nf1, nf2] = noise_floor(data_bp_mont, anno, fs1);
val1a(ii) = mean(nf1); val1b(ii) = mean(nf2);
end
save('results_qa_v1.mat', 'val1a', 'val1b', 'val2');
load results_qa_v1
X = [ages sex]; % convert sex to binary first
y1 = fitlm(X, val1a');
y2 = fitlm(X, val1b');
% SIMPLE FEATURE EXTRACTION
fts = cell(1, length(fnames));
%parpool(8) consider using parpool if possible
for ii = 1:length(fnames)
ii
fts{ii} = get_fts(fnames, ii);
end
%cd('L:\Lab_JamesR\nathanST\FBA_paeds_project\Documents\paper3_open_data')
save('results_qa_feats_v1.mat', 'fts', 'ages', 'sex')
load results_qa_feats_v1
% CORRELATIONS WITH AGE
fv1 = zeros(length(fts), 32); fv2 = fv1;
for ii = 1:length(fts)
fval = fts{ii};
fv1(ii,:) = median(fval(1:10,:));
A = size(fval);
if A(1)<21
fv2(ii,:) = median(fval(11:end,:)); % pre N2 onset
else
fv2(ii,:) = median(fval(11:21,:)); % post N2 onset
end
end
p1 = corr(fv1, ages);
p2 = corr(fv2, ages);
val = unique(ages);
prs = [1:2:length(val) ; 2:2:length(val)];
x1 = ages(sex==0);
x2 = ages(sex==1);
xx = linspace(min(ages), max(ages), 100);
A = size(fv2);
for ii = 1:A(2)
ii
y1 = fv2(sex==0,ii);
y2 = fv2(sex==1,ii);
y1v = zeros(1, length(val)); y2v = y1v;
for jj = 1:length(val)
y1v(jj) = median(y1(x1==val(jj)));
y2v(jj) = median(y2(x2==val(jj)));
end
cohD = zeros(length(prs), 5);
for jj = 1:length(prs)
dum = meanEffectSize(y1(x1==val(prs(1,jj)) | x1==val(prs(2,jj))), y2(x2==val(prs(1,jj)) | x2==val(prs(2,jj))));
cohD(jj,:) = [dum.Variables sum(x1==val(prs(1,jj)) | x1==val(prs(2,jj))) sum(x2==val(prs(1,jj)) | x2==val(prs(2,jj)))];
end
end
% START PLOTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure;
subplot(2,2,1);
f = zeros(1,17);
for ii = 1:17
rf = find(ages>=ii-1 & ages<ii);
f(ii) = median(fv1(rf,25));
end
x = [1:17]-0.5;
B = polyfit(x([1:15 17]), f([1:15 17]),2);
res = fv1(:,25)-polyval(B,ages);
rx = find(abs(res)<5e-4);
plot(ages(rx), fv1(rx,25), '.');
ylabel('Hjorth 2')
xlabel('Age (y)')
axis([-1 17 0 2e-3])
set(gca, 'position', [0.1 0.575 0.375 0.375], 'FontName', 'times', 'FontSize', 12)
title('Pre- N2 onset')
text(0, 1.75e-3, 'A', 'FontName', 'times', 'FontSize', 14)
pp = corr(ages, fv1(:,25), 'type','Spearman');
text(10, 0.25e-3, ['r = ' num2str(pp, '%1.3f')], 'FontName', 'times', 'FontSize', 12)
subplot(2,2,3);
f = zeros(1,17);
for ii = 1:17
rf = find(ages>=ii-1 & ages<ii);
f(ii) = median(fv1(rf,30));
end
x = [1:17]-0.5;
B = polyfit(x([1:15 17]), f([1:15 17]),2);
res = fv1(:,30)-polyval(B,ages);
rx = find(abs(res)<0.02);
plot(ages(rx), fv1(rx,30), '.');
ylabel('Burst Sharpness')
xlabel('Age (y)')
axis([-1 17 -.97 -0.89])
set(gca, 'position', [0.1 0.1 0.375 0.375], 'FontName', 'times', 'FontSize', 12)
text(15, -0.9, 'B', 'FontName', 'times', 'FontSize', 14)
pp = corr(ages, fv1(:,30), 'type','Spearman');
text(0, -0.96, ['r = ' num2str(pp, '%1.3f')], 'FontName', 'times', 'FontSize', 12)
subplot(2,2,2);
f = zeros(1,17);
for ii = 1:17
rf = find(ages>=ii-1 & ages<ii);
f(ii) = median(fv2(rf,16));
end
x = [1:17]-0.5;
B = polyfit(x([1:15 17]), f([1:15 17]),3);
res = fv2(:,16)-polyval(B,ages);
rx = find(abs(res)<5);
plot(ages(rx), fv2(rx,16), '.');
ylabel('Relative Alpha* Power (%)')
xlabel('Age (y)')
axis([-1 17 0 17.5])
set(gca, 'position', [0.6 0.575 0.375 0.375], 'FontName', 'times', 'FontSize', 12)
title('Post- N2 onset')
text(0, 15, 'C', 'FontName', 'times', 'FontSize', 14)
pp = corr(ages, fv2(:,16), 'type','Spearman');
text(10, 2, ['r = ' num2str(pp, '%1.3f')], 'FontName', 'times', 'FontSize', 12)
subplot(2,2,4);
f = zeros(1,17);
for ii = 1:17
rf = find(ages>=ii-1 & ages<ii);
f(ii) = median(fv2(rf,1));
end
x = [1:17]-0.5;
B = polyfit(x([1:15 17]), f([1:15 17]),2);
res = fv2(:,1)-polyval(B,ages);
rx = find(abs(res)<4);
plot(ages, fv2(:,1), '.');
ylabel('Amplitude (5^{th} centile; \muV)')
xlabel('Age (y)')
axis([-1 17 0 15])
set(gca, 'position', [0.6 0.1 0.375 0.375], 'FontName', 'times', 'FontSize', 12)
text(15, 13, 'D', 'FontName', 'times', 'FontSize', 14)
pp = corr(ages, fv1(:,1), 'type','Spearman');
text(0, 2, ['r=' num2str(pp, '%1.3f')], 'FontName', 'times', 'FontSize', 12)
% END PLOTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% TRAIN GPR based age prediction - 5-fold CV 80:20 split
% Find outliers in features
rfs = zeros(1,length(fv1));
for jj = 1:32
y = fv1(:,jj); x = ages;
B = polyfit(x,y,2);
res = y - polyval(B, x);
dum = zeros(1, length(y)); dum(find(abs(res)>6*std(res)))=1;
rfs = rfs+dum;
y = fv2(:,jj); x = ages;
B = polyfit(x,y,2);
res = y - polyval(B, x);
dum = zeros(1, length(y)); dum(find(abs(res)>6*std(res)))=1;
rfs = rfs+dum;
end
% get rid of outliers for now
nref = find(rfs==0);
fv1 = fv1(nref,:);
fv2 = fv2(nref,:);
ages = ages(nref);
% split up data into 5 folds for cross-validation
pid_in1 = 1:length(fv1);
pma_in1 = ages;
Mc = 5;
out = zeros(1,1000);
for qq = 1:1000
rng(qq)
pd = unique(pid_in1);
y = cell(1,Mc);
rx = ones(1,length(pd));
K = floor(length(pd)/Mc); dum = rem(length(pd), Mc);
K = K.*ones(1,Mc); K(1:dum) = K(1)+1;
for ii = 1:Mc
rz = find(rx==1);
dum = randsample(length(rz), K(ii), false);
rx(rz(dum)) = 0;
y{ii} = rz(dum);
end
y{end} = [y{end}' ; find(rx==1)']';
for ii = 1:Mc
y{ii} = pd(y{ii});
end
ss = cell(1, Mc);
for ii = 1:Mc
rf = y{ii};
ag = [];
for jj = 1:length(rf); ag = [ag pma_in1(find(pid_in1==rf(jj)))]; end
ss{ii} = ag;
end
pv = zeros(1,6); c1 = 1;
for ii = 1:Mc
for jj = ii+1:Mc
[~, pv(c1)] = kstest2(ss{ii}, ss{jj});
c1 = c1+1;
end
end
out(qq) = mean(pv);
end
nr = find(out==max(out)); % nr = 7
rng(nr)
yr = cell(1,Mc);
rx = ones(1,length(pd)); %rt = 1-rx; %rr = 1:length(pid);
for ii = 1:Mc
rz = find(rx==1);
dum = randsample(length(rz), K(ii), false);
rx(rz(dum)) = 0;
yr{ii} = rz(dum);
end
yr{end} = [yr{end}' ; find(rx==1)']';
for ii = 1:Mc; yr{ii} = pd(yr{ii}); end
% do training and testing of age prediction for both pre- and post- N2
% onset data
pred1 = []; pred2 = []; agx = [];
for ii = 1:Mc
ii
dum = zeros(1,length(fv1));
dum(yr{ii})=1;
r1 = find(dum==1); r2 = find(dum==0);
rGP1 = fitrgp(fv1(r2,:), ages(r2), 'BasisFunction', 'constant', 'KernelFunction', 'matern52', 'Standardize', true);
out1 = predict(rGP1, fv1(r1,:));
pred1 = [pred1 ; out1];
rGP2 = fitrgp(fv2(r2,:), ages(r2), 'BasisFunction', 'constant', 'KernelFunction', 'matern52', 'Standardize', true);
out2 = predict(rGP1, fv2(r1,:));
pred2 = [pred2 ; out2];
agx = [agx ; ages(r1)];
end
% START PLOTTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure;
subplot(1,2,1)
plot(agx, pred1 ,'.')
title('Pre- N2 Onset')
set(gca, 'FontName', 'times', 'Fontsize', 16)
xlabel('Age (y)'); ylabel('Predicted Age (y)')
grid on; hold on;
plot([-2 18], [-2, 18], 'k')
axis([-1 17 -1, 17])
set(gca, 'position', [0.075 0.15 0.4 0.8], 'Xtick', [0:2:16], 'Ytick', [0:2:16])
%text(0, 16, 'A', 'FontName', 'times', 'Fontsize', 14)
pp = corr(agx, pred1);
text(10, 2, ['r = ' num2str(pp, '%1.3f')], 'FontName', 'times', 'FontSize', 16)
subplot(1,2,2)
plot(agx, pred2 ,'.')
title('Post- N2 Onset')
set(gca, 'FontName', 'times', 'Fontsize', 16)
xlabel('Age (y)'); ylabel('Predicted Age (y)')
grid on; hold on;
plot([-2 18], [-2, 18], 'k')
axis([-1 17 -1, 17])
set(gca, 'position', [0.575 0.15 0.4 0.8], 'Xtick', [0:2:16], 'Ytick', [0:2:16])
%text(0, 16, 'B', 'FontName', 'times', 'Fontsize', 14)
pp = corr(agx, pred2);
text(10, 2, ['r = ' num2str(pp, '%1.3f')], 'FontName', 'times', 'FontSize', 16)
% END PLOTTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% DO SPECTRAL ANALYSIS
addpath('L:\Lab_JamesR\nathanST\FBA_paeds_project\Code\fba_code_107052022\subfunctions')
spa = cell(1, length(fnames)); spb = spa;
%parpool(6)
for ii = 1:length(fnames)
ii
[spa{ii}, spb{ii}] = get_spectra(fnames, ii);
end
save('results_qa_spec_v1.mat', 'spa', 'spb', 'ages', 'sex')
load results_qa_spec_v1
rn = [0 1 ; 1 3 ; 3 7 ; 7 17];
d1 = 10*log10(cell2mat(spa')); d2 = 10*log10(cell2mat(spb'));
spec_pre_mean = zeros(4, 257); spec_pre_std = spec_pre_mean; spec_post_mean = spec_pre_mean;
spec_post_std = spec_pre_mean;
for ii = 1:4
rf = find(ages> rn(ii,1) & ages <= rn(ii,2));
length(rf)
spec_pre_mean(ii,:) = mean(d1(rf,:));
spec_pre_std(ii,:) = std(d1(rf,:));
spec_post_mean(ii,:) = mean(d2(rf,:));
spec_post_std(ii,:) = std(d2(rf,:));
end
% START PLOTTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure; set(gcf, 'Position', [1000 150 800 680])
subplot(4,4,[1 2 5 6]); hold on;
f = linspace(0, 32, 257); rf = 6:255;
for ii = 1:4
h(ii) = plot(f(rf), spec_pre_mean(ii,rf), 'LineWidth', 2);
end
title('Pre- N2 Onset')
axis([0 32 -10 40])
legend(h, '0-1y','1-3y','3-7y','7-16y')
set(gca, 'Fontname', 'times', 'fontsize', 14, 'Xtick', [0:4:32], 'XTickLabel', [0:4:32])
xlabel('Frequency (Hz)', 'Fontname', 'times', 'fontsize', 16);
ylabel('Power (dB)', 'Fontname', 'times', 'fontsize', 16);
grid on
set(gca, 'Position', [0.075 0.575 0.405 0.4])
%text(4, 35, 'A', 'Fontname', 'times', 'fontsize', 18)
fr = [9 10 13 14];
pst1(1,:) = [0.075 0.285 0.19 0.2];
pst1(2,:) = [0.295 0.285 0.19 0.2];
pst1(3,:) = [0.075 0.075 0.19 0.2];
pst1(4,:) = [0.295 0.075 0.19 0.2];
for ii = 1:4
subplot(4,4, fr(ii))
cc = get(h(ii), 'color');
g1 = spec_pre_mean(ii,:)+2*spec_pre_std(ii,:);
g2 = spec_pre_mean(ii,:)-2*spec_pre_std(ii,:);
j = patch([f(rf(1)) f(rf) f(rf(250:-1:1)) f(rf(1))], [g2(rf(1)) g1(rf) g2(rf(250:-1:1)) g1(rf(1))], cc);
set(j, "EdgeColor", 'none')
hold on;
plot(f(rf), spec_pre_mean(ii,rf), 'color', [1 1 1]);
axis([0 32 0 40])
set(gca, 'Position', pst1(ii,:),'Xtick', [0:8:32], 'Fontname', 'times', 'fontsize', 14)
switch ii
case 1
set(gca, 'Xticklabel', {}, 'Fontname', 'times', 'fontsize', 14)
ylabel('Power (dB)', 'Fontname', 'times', 'fontsize', 16);
text(20, 32, '0-1y', 'Fontname', 'times', 'fontsize', 14)
case 2
set(gca, 'Yticklabel', {}, 'Xticklabel', {}, 'Fontname', 'times', 'fontsize', 14)
text(20, 32, '1-3y', 'Fontname', 'times', 'fontsize', 14)
case 3
set(gca, 'Fontname', 'times', 'fontsize', 14)
xlabel('Frequency (Hz)', 'Fontname', 'times', 'fontsize', 16);
ylabel('Power (dB)', 'Fontname', 'times', 'fontsize', 16);
text(20, 32, '3-7y', 'Fontname', 'times', 'fontsize', 14)
case 4
set(gca, 'Yticklabel', {}, 'Fontname', 'times', 'fontsize', 14)
xlabel('Frequency (Hz)', 'Fontname', 'times', 'fontsize', 16);
text(20, 32, '7-16y', 'Fontname', 'times', 'fontsize', 14)
end
grid on;
axis([0 32 -10 40])
end
subplot(4,4,[3 4 7 8]);
hold on;
f = linspace(0, 32, 257); rf = 6:255;
for ii = 1:4
h1(ii) = plot(f(rf), spec_post_mean(ii,rf), 'LineWidth', 2);
end
title('Post- N2 Onset')
axis([0 32 -10 40])
legend(h1, '0-1y','1-3y','3-7y','7-16y')
set(gca, 'Fontname', 'times', 'fontsize', 14, 'Xtick', [0:4:32], 'XTickLabel', [0:4:32])
xlabel('Frequency (Hz)', 'Fontname', 'times', 'fontsize', 16);
ylabel('Power (dB)', 'Fontname', 'times', 'fontsize', 16);
grid on
set(gca, 'Position', [0.575 0.575 0.405 0.4])
%text(4, 35, 'B', 'Fontname', 'times', 'fontsize', 18)
fr = [9 10 13 14];
pst1(1,:) = [0.575 0.285 0.19 0.2];
pst1(2,:) = [0.795 0.285 0.19 0.2];
pst1(3,:) = [0.575 0.075 0.19 0.2];
pst1(4,:) = [0.795 0.075 0.19 0.2];
for ii = 1:4
subplot(4,4, fr(ii))
cc = get(h1(ii), 'color');
g1 = spec_post_mean(ii,:)+2*spec_post_std(ii,:);
g2 = spec_post_mean(ii,:)-2*spec_post_std(ii,:);
j = patch([f(rf(1)) f(rf) f(rf(250:-1:1)) f(rf(1))], [g2(rf(1)) g1(rf) g2(rf(250:-1:1)) g1(rf(1))], cc);
set(j, "EdgeColor", 'none')
hold on;
plot(f(rf), spec_post_mean(ii,rf), 'color', [1 1 1]);
axis([0 32 0 40])
set(gca, 'Position', pst1(ii,:),'Xtick', [0:8:32], 'Fontname', 'times', 'fontsize', 14)
switch ii
case 1
set(gca, 'Xticklabel', {}, 'Fontname', 'times', 'fontsize', 14)
ylabel('Power (dB)', 'Fontname', 'times', 'fontsize', 16);
text(20, 32, '0-1y', 'Fontname', 'times', 'fontsize', 14)
case 2
set(gca, 'Yticklabel', {}, 'Xticklabel', {}, 'Fontname', 'times', 'fontsize', 14)
text(20, 32, '1-3y', 'Fontname', 'times', 'fontsize', 14)
case 3
set(gca, 'Fontname', 'times', 'fontsize', 14)
xlabel('Frequency (Hz)', 'Fontname', 'times', 'fontsize', 16);
ylabel('Power (dB)', 'Fontname', 'times', 'fontsize', 16);
text(20, 32, '3-7y', 'Fontname', 'times', 'fontsize', 14)
case 4
set(gca, 'Yticklabel', {}, 'Fontname', 'times', 'fontsize', 14)
xlabel('Frequency (Hz)', 'Fontname', 'times', 'fontsize', 16);
text(20, 32, '7-16y', 'Fontname', 'times', 'fontsize', 14)
end
grid on;
axis([0 32 -10 40])
end
% END PLOTTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Plot some example EEG recordings
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
filename = 'anon_codex_age_sex.csv';
% Specify range and delimiter
dataLines = [2, Inf];
opts = delimitedTextImportOptions("NumVariables", 3);
opts.DataLines = dataLines;
opts.Delimiter = ",";
opts.VariableNames = ["AnonFilename", "QuantizedAgeyears", "QuantizedSex"];
opts.VariableTypes = ["string", "double", "categorical"];
opts.ExtraColumnsRule = "ignore";
opts.EmptyLineRule = "read";
opts = setvaropts(opts, "AnonFilename", "WhitespaceRule", "preserve");
opts = setvaropts(opts, ["AnonFilename", "QuantizedSex"], "EmptyFieldRule", "auto");
% Import the data
anoncodex = readtable(filename, opts);
fnames = cellstr(anoncodex{:,1});
ages = anoncodex{:,2};
sex = anoncodex{:,3};
ref1 = find(ages>7 & ages<=20);
% BASIC EVALUATIONS
fs1 = 250;
[B,A] = butter(4, [1 64]./fs1, 'bandpass');
r1 = fs1*5*60+1; r2 = r1+8*fs1-1;
cc(1,:) = [1 0.25 0.25];
cc(2,:) = [1 0.25 0.25];
cc(3,:) = [1 0.25 0.25];
cc(4,:) = [1 0.25 0.25];
cc(5,:) = [0.1 0.6 1];
cc(6,:) = [0.1 0.6 1];
cc(7,:) = [0.1 0.6 1];
cc(8,:) = [0.1 0.6 1];
cc(9,:) = [0.85 0 0];
cc(10,:) = [0.85 0 0];
cc(11,:) = [0.85 0 0];
cc(12,:) = [0.85 0 0];
cc(13,:) = [0 0 0.96];
cc(14,:) = [0 0 0.96];
cc(15,:) = [0 0 0.96];
cc(16,:) = [0 0 0.96];
cc(17,:) = [0 0 0];
cc(18,:) = [0 0 0];
fr = [8 57 694 158];
str{1} = 'Fp2-F4'; % Fp2-F4, Fp2-F4
str{2} = 'F4-C4'; % F4-C4, F4-C4
str{3} = 'C4-P4'; % C4-P4, C4-P4
str{4} = 'P4-O2'; % P4-O2, P4-O2
str{5} = 'Fp1-F3'; % Fp1-F3, Fp1-F3
str{6} = 'F3-C3'; % F3-C3, F3-C3
str{7} = 'C3-P3'; % C3-P3, C3-P3
str{8} = 'P3-O1'; % P3-O1, P3-O1
str{9} = 'Fp2-F8'; % Fp2-F8, Fp2-F8
str{10} = 'F8-T4'; % F8-T4, F8-T8
str{11} = 'T4-T6'; % T4-T6, T8-P8
str{12} = 'T6-O2'; % T6-O2, P8-O2
str{13} = 'Fp1-F7'; % Fp1-F7, Fp1-F7
str{14} = 'F7-T3'; % F7-T3, F7-T7
str{15} = 'T3-T5'; % T3-T5, T7-P7
str{16} = 'T5-O1'; % T5-O1, P7-O1
str{17} = 'Fz-Cz'; % Fz-Cz, Fz-Cz
str{18} = 'Cz-Pz'; % Cz-Pz, Cz-Pz
str{19} = 'ECG';
% START PLOTTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure
subplot(2,2,1); hold on
[dat, hdr, label, fs, scle, offs] = read_edf(fnames{fr(1)});
[data_bp_mont, ecg] = read_into_montage(dat, label, scle, fs, fs1, 0);
dbp = filtfilt(B,A, data_bp_mont')';
t = linspace(0,8, length(r1:r2));
for jj = 1:18
plot(t, dbp(jj,r1:r2)-jj*100, 'color', cc(jj,:))
end
plot(t, (ecg(r1:r2)-median(ecg(r1:r2)))./10-(jj+1)*100, 'Color', [0 0.6 0])
set(gca, 'Ytick', [-1900:100:-100], 'Yticklabel', str(19:-1:1), 'Xticklabel', {})
axis([0 8 -2000 0])
set(gca, 'Position', [0.075 0.535 0.435 0.45], 'fontname', 'times', 'fontsize', 10)
text(0.175, -150, 'A', 'BackgroundColor', [1 1 1], 'fontname', 'times', 'fontsize', 14)
subplot(2,2,2); hold on
[dat, hdr, label, fs, scle, offs] = read_edf(fnames{fr(2)});
[data_bp_mont, ecg] = read_into_montage(dat, label, scle, fs, fs1, 0);
dbp = filtfilt(B,A, data_bp_mont')';
t = linspace(0,8, length(r1:r2));
for jj = 1:18
plot(t, dbp(jj,r1:r2)-jj*100, 'color', cc(jj,:))
end
plot(t, (ecg(r1:r2)-median(ecg(r1:r2)))./20-(jj+1)*100, 'Color', [0 0.6 0])
set(gca, 'Ytick', [-1900:100:-100], 'Yticklabel', {}, 'Xticklabel', {})
axis([0 8 -2000 0])
set(gca, 'Position', [0.55 0.535 0.435 0.45], 'fontname', 'times', 'fontsize', 10)
text(0.175, -150, 'B', 'BackgroundColor', [1 1 1], 'fontname', 'times', 'fontsize', 14)
subplot(2,2,3); hold on
[dat, hdr, label, fs, scle, offs] = read_edf(fnames{fr(3)});
[data_bp_mont, ecg] = read_into_montage(dat, label, scle, fs, fs1, 0);
dbp = filtfilt(B,A, data_bp_mont')';
t = linspace(0,8, length(r1:r2));
for jj = 1:18
plot(t, dbp(jj,r1:r2)-jj*100, 'color', cc(jj,:))
end
plot(t, (ecg(r1:r2)-median(ecg(r1:r2)))./10-(jj+1)*100, 'Color', [0 0.6 0])
set(gca, 'Ytick', [-1900:100:-100], 'Yticklabel', str(19:-1:1))
axis([0 8 -2000 0])
set(gca, 'Position', [0.075 0.075 0.435 0.45], 'fontname', 'times', 'fontsize', 10)
xlabel('time (s)')
text(0.175, -150, 'C', 'BackgroundColor', [1 1 1], 'fontname', 'times', 'fontsize', 14)
subplot(2,2,4); hold on
[dat, hdr, label, fs, scle, offs] = read_edf(fnames{fr(4)});
[data_bp_mont, ecg] = read_into_montage(dat, label, scle, fs, fs1, 0);
dbp = filtfilt(B,A, data_bp_mont')';
t = linspace(0,8, length(r1:r2));
for jj = 1:18
plot(t, dbp(jj,r1:r2)-jj*100, 'color', cc(jj,:))
end
plot(t, (ecg(r1:r2)-median(ecg(r1:r2)))./10-(jj+1)*100, 'Color', [0 0.6 0])
set(gca, 'Ytick', [-1900:100:-100], 'Yticklabel', {})
axis([0 8 -2000 0])
set(gca, 'Position', [0.55 0.075 0.435 0.45], 'fontname', 'times', 'fontsize', 10)
xlabel('time (s)')
text(0.175, -150, 'D', 'BackgroundColor', [1 1 1], 'fontname', 'times', 'fontsize', 14)
plot([1.5 1.5 2.5], [-950 -1050 -1050], 'k', 'LineWidth', 1)
% END PLOTTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% ECG ANALYSIS
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
filename = 'anon_codex_age_sex.csv';
% Specify range and delimiter
dataLines = [2, Inf];
opts = delimitedTextImportOptions("NumVariables", 3);
opts.DataLines = dataLines;
opts.Delimiter = ",";
opts.VariableNames = ["AnonFilename", "QuantizedAgeyears", "QuantizedSex"];
opts.VariableTypes = ["string", "double", "categorical"];
opts.ExtraColumnsRule = "ignore";
opts.EmptyLineRule = "read";
opts = setvaropts(opts, "AnonFilename", "WhitespaceRule", "preserve");
opts = setvaropts(opts, ["AnonFilename", "QuantizedSex"], "EmptyFieldRule", "auto");
% Import the data
anoncodex = readtable(filename, opts);
fnames = cellstr(anoncodex{:,1});
ages = anoncodex{:,2};
sex = anoncodex{:,3};
% BASIC EVALUATIONS
load('artefact_detection_models.mat');
fs1 = 250; fs2 = 12; epl = 5*60*fs1; olap = 30*fs1;
[Bn0,An0] = butter(4, 2*2/fs1, 'high');
[Bn1,An1] = butter(2, 2*[48 52]/fs1 , 'stop');
[Bn2,An2] = butter(2, 2*[98 102]/fs1 , 'stop');
% CALCULATE HRV FEATURES FROM 5-minute epochs of ECG
tadj = fs1*10; % correct for any poential end effects
feat = cell(length(fnames),2);
for ii = 1:length(fnames)
ii
[dat, hdr, label, fs, scle, offs] = read_edf(fnames{ii});
[ecg, fs] = read_into_montage_ecg(dat, label, scle, fs);
ecg = resample(ecg, fs1, fs);
ecg = filtfilt(Bn1, An1, ecg); ecg = filtfilt(Bn2, An2, ecg); % notch at 50 notch at 100
ecg = filtfilt(Bn0,An0, ecg);
block_no = floor(length(ecg)/olap)-epl/olap+1; fts = NaN*ones(block_no,38);
MM = (floor(length(ecg)/fs1/2))*2;
flag1 = 0; flag2 = 0;
fts = NaN*ones(block_no, 38); ag = NaN*ones(block_no,1);
try
for jj = 1:block_no
r1 = (jj-1)*olap+1-tadj; r2 = r1+epl-1+2*tadj;
if r1<1; r1 = 1; flag1 = 1; end; if r2>MM*fs1; r2 = MM*fs1; flag2 = 1; end
ecg1 = ecg(r1:r2); ecg2 = ecg((jj-1)*olap+1:(jj-1)*olap+epl);
if flag1 == 1; ecg1 = [zeros(1,tadj) ecg1]; end
if flag2 == 1; ecg1 = [ecg1 zeros(1,tadj)]; end
qts = quantile(ecg1, [0.01 0.99]); if abs(qts(1))>abs(qts(2)); ecg1 = -ecg1; end
qts = quantile(ecg2, [0.01 0.99]); if abs(qts(1))>abs(qts(2)); ecg2 = -ecg2; end
afts = calculate_features_ecg(ecg2, fs1);
[~, out1] = predict(Mdl1, afts); [~, out2] = predict(Mdl2, afts); [~, out3] = predict(Mdl3, afts);
if (out1(2) < 0) && (out2(2) < 0.5) && (out3(2) < 0)
[~, rr, ~] = pan_tompkin_adapt(ecg1,fs1);
rr(rr<=tadj)=0; rr(rr>=epl+tadj)=0;
rr = rr(rr>0);
if (length(rr)>200) && (length(rr)<900)
fts(jj,:) = calculate_features_valid(rr, fs);
ag(jj) = ages(ii);
end
end
end
feat{ii,1} = fts;
feat{ii,2} = ag;
catch
end
end
save('ecg_features_online_data.mat', 'feat', '-v7.3');
load('ecg_features_online_data.mat');
% Find outliers
fv1 = NaN*ones(length(feat), 38); fv2 = fv1; age = zeros(length(feat),1);
for ii = 1:length(feat)
fts = feat{ii,1};
fv1(ii,:) = median(fts(1:2,:));
fv2(ii,:) = median(fts(11:end,:));
age(ii) = feat{ii,2}(1);
end
rf = find(~isnan(age) & ~isnan(sum(fv1'))' & ~isnan(sum(fv2'))');
fv1 = fv1(rf,:);
fv2 = fv2(rf,:);
age = age(rf,:);
rfs = zeros(1,length(fv1));
for jj = 1:38
y = fv1(:,jj); x = age;
B = polyfit(x,y,2);
res = y - polyval(B, x);
dum = zeros(1, length(y)); dum(find(abs(res)>6*std(res)))=1;
rfs = rfs+dum;
y = fv2(:,jj); x = age;
B = polyfit(x,y,2);
res = y - polyval(B, x);
dum = zeros(1, length(y)); dum(find(abs(res)>6*std(res)))=1;
rfs = rfs+dum;
end
% remove outliers
nref = find(rfs==0);
fv1 = fv1(nref,:);
fv2 = fv2(nref,:);
age = age(nref);
% TRAIN GPR age predictor - 5 fold CV 80:20 split
pid_in1 = 1:length(fv1);
pma_in1 = age;
Mc = 5;
out = zeros(1,1000);
for qq = 1:1000
rng(qq)
pd = unique(pid_in1);
y = cell(1,Mc);
rx = ones(1,length(pd)); %rt = 1-rx; %rr = 1:length(pid);
K = floor(length(pd)/Mc); dum = rem(length(pd), Mc);
K = K.*ones(1,Mc); K(1:dum) = K(1)+1;
for ii = 1:Mc
rz = find(rx==1);
dum = randsample(length(rz), K(ii), false);
rx(rz(dum)) = 0;
y{ii} = rz(dum);
end
y{end} = [y{end}' ; find(rx==1)']';
for ii = 1:Mc
y{ii} = pd(y{ii});
end
ss = cell(1, Mc);
for ii = 1:Mc
rf = y{ii};
ag = [];
for jj = 1:length(rf); ag = [ag pma_in1(find(pid_in1==rf(jj)))]; end
ss{ii} = ag;
end
pv = zeros(1,6); c1 = 1;
for ii = 1:Mc
for jj = ii+1:Mc
[~, pv(c1)] = kstest2(ss{ii}, ss{jj});
c1 = c1+1;
end
end
out(qq) = mean(pv);
end
nr = find(out==max(out)); % nr = 7
rng(nr)
yr = cell(1,Mc);
rx = ones(1,length(pd)); %rt = 1-rx; %rr = 1:length(pid);
for ii = 1:Mc
rz = find(rx==1);
dum = randsample(length(rz), K(ii), false);
rx(rz(dum)) = 0;
yr{ii} = rz(dum);
end
yr{end} = [yr{end}' ; find(rx==1)']';
for ii = 1:Mc; yr{ii} = pd(yr{ii}); end
pred1 = []; pred2 = []; agx = [];
for ii = 1:Mc
ii
dum = zeros(1,length(fv1));
dum(yr{ii})=1;
r1 = find(dum==1); r2 = find(dum==0);
rGP1 = fitrgp(fv1(r2,:), age(r2), 'BasisFunction', 'constant', 'KernelFunction', 'matern52', 'Standardize', true);
out1 = predict(rGP1, fv1(r1,:));
pred1 = [pred1 ; out1];
rGP2 = fitrgp(fv2(r2,:), age(r2), 'BasisFunction', 'constant', 'KernelFunction', 'matern52', 'Standardize', true);
out2 = predict(rGP1, fv2(r1,:));
pred2 = [pred2 ; out2];
agx = [agx ; age(r1)];
end
% START PLOTTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure;
subplot(1,2,1)
plot(agx, pred1 ,'.')
title('Pre- N2 Onset')
set(gca, 'FontName', 'times', 'Fontsize', 16)
xlabel('Age (y)'); ylabel('Predicted Age (y)')
grid on; hold on;
plot([-2 18], [-2, 18], 'k')
axis([-1 17 -1, 17])
set(gca, 'position', [0.075 0.15 0.4 0.8], 'Xtick', [0:2:16], 'Ytick', [0:2:16])
%text(0, 16, 'A', 'FontName', 'times', 'Fontsize', 14)
pp = corr(agx(~isnan(agx)), pred1(~isnan(agx)));
text(10, 2, ['r = ' num2str(pp, '%1.3f')], 'FontName', 'times', 'FontSize', 16)
subplot(1,2,2)
plot(agx, pred2 ,'.')
title('Post- N2 Onset')
set(gca, 'FontName', 'times', 'Fontsize', 16)
xlabel('Age (y)'); ylabel('Predicted Age (y)')
grid on; hold on;
plot([-2 18], [-2, 18], 'k')
axis([-1 17 -1, 17])
set(gca, 'position', [0.575 0.15 0.4 0.8], 'Xtick', [0:2:16], 'Ytick', [0:2:16])
%text(0, 16, 'B', 'FontName', 'times', 'Fontsize', 14)
pp = corr(agx(~isnan(agx)), pred2(~isnan(agx)));
text(10, 2, ['r = ' num2str(pp, '%1.3f')], 'FontName', 'times', 'FontSize', 16)
% END PLOTTTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% just artefact stuff
filename = 'anon_codex_age_sex.csv';
% Specify range and delimiter
dataLines = [2, Inf];
opts = delimitedTextImportOptions("NumVariables", 3);
opts.DataLines = dataLines;
opts.Delimiter = ",";
opts.VariableNames = ["AnonFilename", "QuantizedAgeyears", "QuantizedSex"];
opts.VariableTypes = ["string", "double", "categorical"];
opts.ExtraColumnsRule = "ignore";
opts.EmptyLineRule = "read";
opts = setvaropts(opts, "AnonFilename", "WhitespaceRule", "preserve");
opts = setvaropts(opts, ["AnonFilename", "QuantizedSex"], "EmptyFieldRule", "auto");
% Import the data
anoncodex = readtable(filename, opts);
fnames = cellstr(anoncodex{:,1});
ages = anoncodex{:,2};
sex = anoncodex{:,3};
% BASIC EVALUATIONS
load('artefact_detection_models.mat');
fs1 = 250; fs2 = 12; epl = 5*60*fs1; olap = epl;
[Bn0,An0] = butter(4, 2*2/fs1, 'high');
[Bnx,Anx] = butter(4, 1/fs1, 'high');
[Bn1,An1] = butter(2, 2*[48 52]/fs1 , 'stop');
[Bn2,An2] = butter(2, 2*[98 102]/fs1 , 'stop');
W = floor(fs1/3);
tadj = fs1*10; % correct for any poential end effects
feat = cell(length(fnames),3);
fband = [0.5 1 ; 1 3 ; 3 25 ; 25 125]; df = diff(fband');
for ii = 1:length(fnames)
ii
[dat, hdr, label, fs, scle, offs] = read_edf(fnames{ii});
[ecg, fs] = read_into_montage_ecg(dat, label, scle, fs);
ecg = resample(ecg, fs1, fs);
ecg = filtfilt(Bn1, An1, ecg); ecg = filtfilt(Bn2, An2, ecg); % notch at 50 notch at 100
ecg0 = filtfilt(Bnx, Anx, ecg);
ecg = filtfilt(Bn0,An0, ecg);
block_no = floor(length(ecg)/olap)-epl/olap+1; fts = NaN*ones(block_no,38);
MM = (floor(length(ecg)/fs1/2))*2;
flag1 = 0; flag2 = 0;
fts = NaN*ones(block_no, 38); ag = NaN*ones(block_no,1);
try
artx1 = []; artx2 = []; artx3 = []; c1 = 0;
pow = zeros(block_no,4);
for jj = 1:block_no
r1 = (jj-1)*olap+1-tadj; r2 = r1+epl-1+2*tadj;
if r1<1; r1 = 1; flag1 = 1; end; if r2>MM*fs1; r2 = MM*fs1; flag2 = 1; end
ecg1 = ecg(r1:r2); ecg2 = ecg((jj-1)*olap+1:(jj-1)*olap+epl);
%spectral_analysis
[Pxx, f] = pwelch(ecg2, hamming(2^13), 2^12, 2^13, fs1);
for qq = 1:4
rf = find(f>=fband(qq,1) & f<fband(qq,2));
pow(jj,qq) = sum(Pxx(rf))./df(qq);
end
if flag1 == 1; ecg1 = [zeros(1,tadj) ecg1]; end
if flag2 == 1; ecg1 = [ecg1 zeros(1,tadj)]; end
qts = quantile(ecg1, [0.01 0.99]); if abs(qts(1))>abs(qts(2)); ecg1 = -ecg1; end
qts = quantile(ecg2, [0.01 0.99]); if abs(qts(1))>abs(qts(2)); ecg2 = -ecg2; end
afts = calculate_features_ecg(ecg2, fs1);
[~, out1] = predict(Mdl1, afts); [~, out2] = predict(Mdl2, afts); [~, out3] = predict(Mdl3, afts);
if (out1(2) < 0) && (out2(2) < 0.5) && (out3(2) < 0)
c1 = c1+1;
[~, rr, ~, art1, art2, art3] = pan_tompkin_adapt_count_error(ecg1,fs1);
artx1(c1,:) = art1;
artx2(c1,:) = art2;
if isempty(art3); artx3(c1,:) = [0 length(rr)]; else; artx3(c1,:) = [art3 length(rr)]; end
end
end
feat{ii,1} = artx2; feat{ii,2} = artx3;
feat{ii,3} = pow;
catch
end
end
save('artefact_features_online_data.mat', 'feat', '-v7.3');
seg_out = []; nn_adj = []; flag_bad = zeros(length(feat),2); pow = [];
for ii = 1:length(feat)
A1 = size(feat{ii,3}); A2 = size(feat{ii,2});
if isempty(feat{ii,2})
flag_bad(ii,:) = [A1(1) 0];
else
flag_bad(ii,:) = [A1(1) A2(1)];
end
dum = feat{ii,1};
if ~isempty(dum)
dum(isnan(dum(:,2)),2) = 0;
seg_out = [seg_out ; dum];
nn_adj = [nn_adj ; feat{ii,2}];
end
pow = [pow; feat{ii,3}];
end

quality_assesssment_for_github.m at commit 5619620, under MIT · at the source

Overview

Authors: Nathan J. Stevenson¹, Kartik K. Iyer¹, James A. Roberts¹, Eero Ahtola², Leena Lauronen², Sampsa Vanhatalo^2,3,4

Brain Modelling Group, QIMR Berghofer,Brisbane, Australia
Clinical Neurophysiology, New Children’s Hospital, Helsinki University Hospital and University of Helsinki,Helsinki, Finland
Paediatric Research Center, New Children’s Hospital, Helsinki University Hospital,Helsinki, Finland
BABA Center, Department of Physiology, University of Helsinki,Helsinki, Finland

Institutions: QIMR Berghofer Medical Research Institute (Australia); University of Helsinki (Finland); Helsinki University Hospital (Finland)

Journal: Scientific data, volume 13, issue 1, article 1018

Dates: received 24 October 2025; accepted 28 April 2026; published online 5 May 2026

Type: Data paper · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41597-026-07356-3 · PMID 42086555 · PMCID PMC13350081 · OpenAlex W7160335086

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: EEG (modality), other (modality), human (organism), methods / tools (subfield)

Methods: Preprocessing, Evoked potentials, Physiology & signal measures

MeSH: Electrocardiography*, Electroencephalography*, Sleep*, Sleep Stages*, Adolescent, Brain, Child, Child, Preschool, Humans, Infant (* major topic)

Journal subjects: Data Descriptor

Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: National Health and Medical Research Council (2002135); Research Council of Finland (335788)

Citations: cited by 2 papers (Europe PMC); 16 references in the paper

Abstract

Paediatric brain activity can be measured effectively during light sleep; a vigilance state that manifests with similar phenomenology on the EEG across childhood. Here, we describe a curated dataset of EEG and ECG recordings from 1032 subjects from 2 months to 16 years of age (Helsinki Kids 1 K – HK1K). All subjects had age-appropriate EEG and ECG recordings, along with typical neurodevelopment, as confirmed by a clinical review of their medical records over the four years following the recording. These data can be used to define normative ranges of paediatric EEG/ECG, train foundation models of paediatric EEG/ECG, and generate age prediction algorithms that underpin measures of brain age gap.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

nstevensonUH/EEG_release_supporting_code

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 56196206b14d9d100f88a428efd65c73ea1e3b40, 15 October 2025

Languages: MATLAB (33)

Size: 36 files, 33 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Signal Processing Toolbox (9 files), Statistics and Machine Learning Toolbox (3 files)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

35 files

anonymize_dataset_for_github.m, MATLAB, 169 lines, 1 match
quality_assesssment_for_github.m, MATLAB, 897 lines, 5 matches
subfunctions/CondEn.m, MATLAB, 39 lines
subfunctions/FuzzyEn.m, MATLAB, 73 lines
subfunctions/GetGH.m, MATLAB, 114 lines
subfunctions/Higuchi1Dn.m, MATLAB, 82 lines
subfunctions/PLmeasure.m, MATLAB, 75 lines
subfunctions/PermEn.m, MATLAB, 87 lines
subfunctions/SampEn.m, MATLAB, 47 lines
subfunctions/ShannonEn.m, MATLAB, 75 lines
subfunctions/binary_seq_to_string.m, MATLAB, 30 lines
subfunctions/calc_lz_complexity.m, MATLAB, 341 lines
subfunctions/calculate_features_valid.m, MATLAB, 45 lines
subfunctions/estimate_hurst_exponent.m, MATLAB, 57 lines
subfunctions/get_fts.m, MATLAB, 14 lines
subfunctions/get_spectra.m, MATLAB, 35 lines
subfunctions/linplot.m, MATLAB, 24 lines
subfunctions/lognorm.m, MATLAB, 7 lines
subfunctions/multiscale_entropy.m, MATLAB, 13 lines
subfunctions/nlin_energy.m, MATLAB, 15 lines
subfunctions/noise_floor.m, MATLAB, 22 lines
subfunctions/pan_tompkin.m, MATLAB, 393 lines
subfunctions/pan_tompkin_adapt.m, MATLAB, 428 lines
subfunctions/pan_tompkin_adapt_count_error.m, MATLAB, 434 lines
subfunctions/per_ha_art.m, MATLAB, 10 lines
subfunctions/poincare.m, MATLAB, 18 lines
subfunctions/read_edf.m, MATLAB, 82 lines
subfunctions/read_into_montage.m, MATLAB, 100 lines, 2 matches
subfunctions/read_into_montage_ecg.m, MATLAB, 28 lines
subfunctions/rr_count.m, MATLAB, 16 lines
subfunctions/single_channel_features.m, MATLAB, 177 lines, 1 match
subfunctions/tf_correlation.m, MATLAB, 77 lines
subfunctions/write_edf.m, MATLAB, 52 lines
LICENSE, License, 21 lines
README.md, Text, 13 lines

At the source: github.com/nstevensonUH/EEG_release_supporting_code

sccn.ucsd.edu/eeglab/index.php

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: the link answers

Software Heritage: not checked

Found in: the text, “Usage Notes”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)

28 September 2026: the link answers (HTTP 200)

At the source: sccn.ucsd.edu/eeglab/index.php

Code availability

MATLAB code that assists in accessing the EEG and ECG recordings within this dataset, and reproduces all analyses undertaken in this data descriptor, is available on Github (https://github.com/nstevensonUH/EEG_release_supporting_code).

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

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
33 scripts, each with its path and the digest of its content;
9 matches 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

Datasets cited

zenodo:17138538, at Zenodo; found in DataCite
zenodo:17138539, at Zenodo; found in “Data availability”

Data availability

The dataset is freely available to access at Zenodo (10.5281/zenodo.17138539).

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 10 MeSH terms, 2 funders, 15 references.

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This paper

Stevenson, N. J., Iyer, K. K., Roberts, J. A., Ahtola, E., Lauronen, L., & Vanhatalo, S. (2026). A dataset of EEG and ECG recordings around the onset of NREM sleep from infancy to adolescence. Scientific data, 13(1), 1018. https://doi.org/10.1038/s41597-026-07356-3

BibTeX

@article{stevenson2026dataset, author = {Stevenson, Nathan J. and Iyer, Kartik K. and Roberts, James A. and Ahtola, Eero and Lauronen, Leena and Vanhatalo, Sampsa}, title = {{A dataset of EEG and ECG recordings around the onset of NREM sleep from infancy to adolescence}}, journal = {Scientific data}, year = {2026}, month = may, volume = {13}, number = {1}, pages = {1018}, publisher = {Nature Publishing Group}, issn = {2052-4463}, doi = {10.1038/s41597-026-07356-3}, url = {https://doi.org/10.1038/s41597-026-07356-3}, pmid = {42086555}, pmcid = {PMC13350081} }

RIS

TY - JOUR AU - Stevenson, Nathan J. AU - Iyer, Kartik K. AU - Roberts, James A. AU - Ahtola, Eero AU - Lauronen, Leena AU - Vanhatalo, Sampsa TI - A dataset of EEG and ECG recordings around the onset of NREM sleep from infancy to adolescence T2 - Scientific data J2 - Sci Data PY - 2026 DA - 2026/05/05 VL - 13 IS - 1 SP - 1018 SN - 2052-4463 PB - Nature Publishing Group DO - 10.1038/s41597-026-07356-3 UR - https://doi.org/10.1038/s41597-026-07356-3 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41597-026-07356-3", "type": "article-journal", "title": "A dataset of EEG and ECG recordings around the onset of NREM sleep from infancy to adolescence", "container-title": "Scientific data", "author": [ { "family": "Stevenson", "given": "Nathan J." }, { "family": "Iyer", "given": "Kartik K." }, { "family": "Roberts", "given": "James A." }, { "family": "Ahtola", "given": "Eero" }, { "family": "Lauronen", "given": "Leena" }, { "family": "Vanhatalo", "given": "Sampsa" } ], "container-title-short": "Sci Data", "volume": "13", "issue": "1", "page": "1018", "DOI": "10.1038/s41597-026-07356-3", "PMID": "42086555", "PMCID": "PMC13350081", "ISSN": "2052-4463", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41597-026-07356-3", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 5 ] ] } }

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A dataset of EEG and ECG recordings around the onset of NREM sleep from infancy to adolescence.

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