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Test_Fs.m
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%% Feature extraction
clc
clear all;
close all;
addpath(genpath('F:\Toolbox\eeglab2021.1'))
ictal_or_inter='interictal'; % 'ictal' or 'interictal'
for Patient=1:56
if Patient==1
Patient_initials='060';
modality='seeg';
ictal_trials=[1:3];
inter_ictal_trials=[1:2];
elseif Patient==2
Patient_initials='064';
ictal_trials=[1];
modality='ecog';
inter_ictal_trials=[2];
elseif Patient==3
Patient_initials='065';
modality='ecog';
ictal_trials=[1:3];
inter_ictal_trials=[1:2];
elseif Patient==4
Patient_initials='070';
ictal_trials=[1:3];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==5
Patient_initials='074';
ictal_trials=[1:3];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==6
Patient_initials='075';
ictal_trials=[1];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==7
Patient_initials='080';
ictal_trials=[1:4];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==8
Patient_initials='082';
ictal_trials=[1:5];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==9
Patient_initials='086';
ictal_trials=[1:2];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==10
Patient_initials='087';
ictal_trials=[1:2];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==11
Patient_initials='088';
ictal_trials=[1:3];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==12
Patient_initials='089';
ictal_trials=[1:4];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==13
Patient_initials='094';
ictal_trials=[1:3];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==14
Patient_initials='097';
ictal_trials=[1:5];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==15
Patient_initials='105';
ictal_trials=[1:2];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==16
Patient_initials='106';
ictal_trials=[1:3];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==17
Patient_initials='107';
ictal_trials=[1:5];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==18
Patient_initials='111';
ictal_trials=[1:5];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==19
Patient_initials='112';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==20
Patient_initials='114';
ictal_trials=[1:4];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==21
Patient_initials='116';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==22
Patient_initials='117';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==23
Patient_initials='123';
ictal_trials=[1:4];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==24
Patient_initials='126';
ictal_trials=[1:4];
modality='ecog';
inter_ictal_trials=[1:2];
elseif Patient==25
Patient_initials='130';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==26
Patient_initials='133';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==27
Patient_initials='134';
ictal_trials=[1];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==28
Patient_initials='135';
ictal_trials=[1:2];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==29
Patient_initials='138';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==30
Patient_initials='139';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==31
Patient_initials='140';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==32
Patient_initials='141';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==33
Patient_initials='142';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==34
Patient_initials='144';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==35
Patient_initials='146';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==36
Patient_initials='148';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==37
Patient_initials='150';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==38
Patient_initials='151';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==39
Patient_initials='157';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==40
Patient_initials='158';
ictal_trials=[4];
modality='seeg';
inter_ictal_trials=[];
elseif Patient==41
Patient_initials='160';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==42
Patient_initials='162';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==43
Patient_initials='163';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==44
Patient_initials='164';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
% elseif Patient==45
% Patient_initials='165';
% seizures=[];
% modality='seeg';
% inter_ictal_trials=[1:2];
elseif Patient==45
Patient_initials='166';
ictal_trials=[1:2];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==46
Patient_initials='171';
ictal_trials=[1:4];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==47
Patient_initials='172';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==48
Patient_initials='173';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==49
Patient_initials='177';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==50
Patient_initials='179';
ictal_trials=[1:2];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==51
Patient_initials='180';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==52
Patient_initials='181';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==53
Patient_initials='185';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==54
Patient_initials='187';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==55
Patient_initials='188';
ictal_trials=[1:5];
modality='seeg';
inter_ictal_trials=[1:2];
elseif Patient==56
Patient_initials='190';
ictal_trials=[1:3];
modality='seeg';
inter_ictal_trials=[1:2];
end
% pre_siz_analysis=60000; %ms
% post_siz_analysis=126999; %ms
if strcmp(ictal_or_inter,'ictal')
pre_siz_analysis=15000; %*2ms
post_siz_analysis=15000; %*2ms
window_spans=[10 100 500]; %*2ms
step_size=100; % *2ms
elseif strcmp(ictal_or_inter,'interictal')
pre_siz_analysis=1; %*2ms
% post_siz_analysis=30000; %*2ms
post_siz_analysis=150000; %*2ms
window_spans=[100 500 1000]; %*2ms
step_size=100; % *2ms
end
windows=[1:length([-pre_siz_analysis:post_siz_analysis])]; % e.g., 1:120,000 time points the whole signal
Time_windows=[round((max(window_spans)/step_size)/2):floor((length(windows)/step_size-(max(window_spans)/step_size)/2))]; % e.g., 2:300 sampled time points
chars='_less_time_res'; % '' or '_more_windows'
featuress=[1:8 10:12 14:23];
% Loading the data file
if strcmp(ictal_or_inter,'ictal')
trials=ictal_trials;
elseif strcmp(ictal_or_inter,'interictal')
trials=inter_ictal_trials;
end
for trial=trials
tic
if strcmp(ictal_or_inter,'ictal')
load([Patient_initials,'_SEEG_Seizure',num2str(trial),'.mat'])
onset=event_info.sample(1);
signal=signal(:,onset-pre_siz_analysis:onset+post_siz_analysis);
elseif strcmp(ictal_or_inter,'interictal')
load([Patient_initials,'_SEEG_InterIctal',num2str(trial),'.mat'])
try
signal=signal(:,pre_siz_analysis:post_siz_analysis);
catch
% clc
% [Patient trial]
display('signal shorter than 5 mins!')
windows=[1:size(signal,2)]; % e.g., 1:120,000 time points the whole signal
Time_windows=[round((max(window_spans)/step_size)/2):floor((length(windows)/step_size-(max(window_spans)/step_size)/2))]; % e.g., 2:300 sampled time points
end
end
channels=[1:length(channels_id)];
Fs=header.Fs;
[Patient Fs]
%
% features = nan(max(featuress),length(channels),length(Time_windows), length(window_spans));
% ccc
% t=0;
% for time_windoww=Time_windows
% t=t+1;
% ws=0;
% for window_span=window_spans
% ws=ws+1;
% wind=windows([time_windoww*step_size+1:time_windoww*step_size+window_span]-round(window_span/2));
%
% for feature=featuress % features when evaluating time windows
% ch=0;
% for channel = channels
% ch=ch+1;
% trial_data=signal(channel,wind);
%
% if feature==1
% %% Time features: Sigal mean
% features(feature,ch,t,ws) = mean(trial_data);
%
% elseif feature==2
% %% Signal median
% features(feature,ch,t,ws) = median(trial_data);
%
% elseif feature==3
% %% Signal variance
% features(feature,ch,t,ws) = var(trial_data);
%
% elseif feature==4
% %% Signal skewness
% features(feature,ch,t,ws) = skewness(trial_data);
%
% elseif feature==5
% %% Signal Kurtosis
% features(feature,ch,t,ws) = kurtosis(trial_data);
%
% elseif feature==6
% %% LZ complexity
% threshold = median(trial_data);
% % threshold = mean(data);
% trial_data(trial_data>= threshold)=1;
% trial_data(trial_data< threshold)=0;
% [features(feature,ch,t,ws),~] = calc_lz_complexity(trial_data, 'exhaustive', 1);
%
% elseif feature==7
% %% Higuchi fractal dimension
% maxtime = length(trial_data);
% % Kmax = floor(maxtime./2);
% Kmax = 10;
% features(feature,ch,t,ws) = Higuchi_FD(trial_data,Kmax);
% % % second implementation
% % [HFD2(channel),~,~,~] = HFD_LCALC(data);
% % % thrid implementation
% % HFD3(channel) = hfd(data,Kmax);
%
% elseif feature==8
% %% Katz fractal dimensions
% features(feature,ch,t,ws) = Katz_FD(trial_data);
%
% elseif feature==9
% %% Lyapunov exponent (largest LLE)
% [features(feature,ch,t,ws),~] = lyaprosen(trial_data,0,0);
%
% elseif feature==10
% %% Hurst Exponent
% features(feature,ch,t,ws) = estimate_hurst_exponent(trial_data);
% % HE2(channel) = genhurst(data);
% % HE3(channel) = hurstCC(data);
%
% elseif feature==11
% %% Sample entropy
% trial_dataN=abs(trial_data/max(abs(trial_data)))';
% features(feature,ch,t,ws) = entropy (trial_dataN);
% % Ent2(channel) = SampEn (2,0.2.* std(data),data,1);
%
% % features(feature,channel,t)=sampen(trial_data);
% elseif feature==12
% %% Approximate Entropy
% features(feature,ch,t,ws) = ApEn (2,0.2.* std(trial_data),trial_data,1);
% % Ent4(channel) = approx_entropy(2,0.2.* std(data),data);
%
% elseif feature==13
% %% Within-trial correlation
% numLags=ceil(length(trial_data)./2);
% [acf,lags,~] =autocorr(trial_data,numLags);
% features(feature,ch,t,ws) = mean(acf(2:end));
%
% elseif feature==14
% %% Hjorth complexity
% % this finds spread of the spectrum and represents the change in frequency
% % Hcomplexity
% st_size=1./Fs;
% data_prime=(diff(trial_data)./st_size);
% data_second=(diff(data_prime)./st_size);
% features(feature,ch,t,ws) = (std(data_second).*std(trial_data))./(std(trial_data)).^2;
%
% elseif feature==15
% %% Hmobility
% st_size=1./Fs;
% data_prime=(diff(trial_data)./st_size);
% data_second=(diff(data_prime)./st_size);
% features(feature,ch,t,ws) = std(data_prime)./std(trial_data);
%
% elseif feature==16
% %% Mean Freq
% features(feature,ch,t,ws) = meanfreq(trial_data,Fs);
%
% elseif feature==17
% %% Median Freq
% features(feature,ch,t,ws) = medfreq(trial_data,Fs);
%
% elseif feature==18
% %% Average Freq
% zeroscount=0;
% for i=2:length(trial_data)
% if (trial_data(i)>0 && trial_data(i-1)<0) || (trial_data(i)<0 && trial_data(i-1)>0)
% zeroscount=zeroscount+1;
% end
% end
% features(feature,ch,t,ws) = zeroscount.*(length(trial_data)./Fs);
%
% elseif feature==19
% %% Spectral edge frequency 95%
% if var(trial_data)==0
% features(feature,ch,t,ws) =0;
% else
% Fourier = fft(trial_data)/length(trial_data);
% Fouriers = (abs(Fourier)); % Spectrum
% Fv = linspace(0, 1, fix(length(trial_data)/2)+1)*Fs/2; % Frequency Vector
% Iv = 1:length(Fv); % Index Vector
% IntSpectrum = cumtrapz(Fv, Fouriers(Iv)); % Numeric Integration
% try
% features(feature,ch,t,ws) = interp1(IntSpectrum, Fv, 0.95*IntSpectrum(end), 'linear'); % Interploate To Find ‘SEF’
% catch
% features(feature,ch,t,ws) = Fs*2;
% end
% end
%
% elseif feature==20
% %% Power at Median Freq
% amp = 2*abs(fft(trial_data))/length(trial_data);
% phs = angle(fft(trial_data));
% Fv = linspace(0, 1, fix(length(trial_data)/2)+1)*Fs/2; % Frequency Vector
% Iv = 1:length(Fv); % Index Vector
% fr_des = medfreq(trial_data,Fs); % Desired Frequency
% ampv = amp(Iv); % Trim To Length Of ‘Fv’
% phsv = phs(Iv); % Trim To Length Of ‘Fv’
% ap = [ampv(:) phsv(:)]; % Amplitude & Phase Matrix
% ap_des = interp1(Fv(:), ap, fr_des, 'linear');
% features(feature,ch,t,ws) = ap_des(1);
%
% elseif feature==21
% %% Phase at Median Freq
% amp = 2*abs(fft(trial_data))/length(trial_data);
% phs = angle(fft(trial_data));
% Fv = linspace(0, 1, fix(length(trial_data)/2)+1)*Fs/2; % Frequency Vector
% Iv = 1:length(Fv); % Index Vector
% fr_des = medfreq(trial_data,Fs); % Desired Frequency
% ampv = amp(Iv); % Trim To Length Of ‘Fv’
% phsv = phs(Iv); % Trim To Length Of ‘Fv’
% ap = [ampv(:) phsv(:)]; % Amplitude & Phase Matrix
% ap_des = interp1(Fv(:), ap, fr_des, 'linear');
% features(feature,ch,t,ws) = ap_des(2);
%
% elseif feature==22
% %% Signal power
% features(feature,ch,t,ws) = bandpower(trial_data);
%
% elseif feature==23
% %% H/L energy ratio
% HB_top=97;
% HB_bot=12.4;
% LB_top=12.4;
% LB_bot=3.5;
% % HB_power=bandpower(trial_data,Fs,[HB_bot HB_top]);
% % LB_power=bandpower(trial_data,Fs,[LB_bot LB_top]);
% data_to_process.times=[0:length(trial_data)-1]*(1/Fs);
% data_to_process.data=trial_data;
% data_to_process.srate=Fs;
% data_to_process.nbchan=1;
% data_to_process.trials=1;
% data_to_process.trials=1;
% data_to_process.event=[];
% data_to_process.pnts=length(trial_data);
% tmp_filt=pop_eegfiltnew(data_to_process, HB_bot,HB_top,[],1);
% HB_energy=mean((tmp_filt.data).^2);
% tmp_filt=pop_eegfiltnew(data_to_process, LB_bot,LB_top,[],1);
% LB_energy=mean((tmp_filt.data).^2);
% features(feature,ch,t,ws) = HB_energy./LB_energy;
% % features(feature,ch,t,ws) = HB_power./(HB_power+LB_power);
%
% % elseif feature==28
% % %% Wavelet transform
% % [c,l] = wavedec(trial_data,5,'sym2');
% % [ca5] = appcoef(c,l,'sym2',5);
% % [cd1,cd2,cd3,cd4,cd5] = detcoef(c,l,[1 2 3 4 5]);
% % feature_chosen(channel,1:length([ca5,cd1,cd2,cd3,cd4,cd5]))=[ca5,cd1,cd2,cd3,cd4,cd5];
% %
% % elseif feature==29
% % %% Hilbert transform amplitude
% % Hilb = hilbert(trial_data);
% % feature_chosen(channel,:)=abs(Hilb);
% %
% % elseif feature==30
% % %% Hilbert transform phase
% % Hilb = hilbert(trial_data);
% % feature_chosen(channel,:)=angle(Hilb);
% %
% % elseif feature==31
% % %% Phase-Amplitude Coupling
% % data.trial{1,1}= trial_data;
% % data.time{1,1}= [1:length(trial_data)]./Fs;
% % data.trialinfo=[100]';
% % data.label{1,1}='SampleData';
% % toi=[0.001 1.0]; % time of interest
% % phase=[0.5 12]; % phase(1):2.5:phase(2)
% % ampl=[24 120]; % amp(1):19:amp(2)
% % diag = 'no'; %'yes' or 'no' to turn on or off diagrams during computation
% % surrogates = 'no'; %'yes' or 'no' to turn on or off surrogates during computation
% % approach = 'tort';%,'ozkort','canolty','PLV';
% % [MI_matrix_raw,~] = calc_MI(data,toi,phase,ampl,diag,surrogates,approach);
% % feature_chosen(channel,:)=reshape(MI_matrix_raw(1:6,1:5),[30 1]);
% %
% % elseif feature==32
% % %% Inter-channel correlation 31 feature per trial
% % ICC=zeros(size(signal,1),1);
% % for ch2=1:size(signal,1)
% % trial_data2=signal(ch2,wind);
% % if band>1
% % trial_data2=eegfilt(trial_data2,Fs,lowband,highband,0,floor(length(trial_data2)/3),0,'fir1');
% % end
% % ICC(ch2,1)=corr(trial_data',trial_data2');
% % end
% % feature_chosen(channel,:)=ICC;
% %
% % elseif feature==33
% % %% CNN
% % trial_data=(trial_data+abs(min(trial_data)));
% % trial_data=(trial_data).*(255./max(trial_data));
% %
% % im_ = single(uint8(trial_data));
% % im_ = repmat(im_,[1 ceil(227*227./length(im_))]);
% % im_=im_(1,1:227*227);
% % im_=reshape(im_,[227 227]);
% % im_ = imresize(im_,net.meta.normalization.imageSize(1:2));
% % im_ = im_ - net.meta.normalization.averageImage;
% %
% % res = vl_simplenn(net, im_);
% % feature_chosen(channel,:)=squeeze(res.x);
% %
% % elseif feature==34
% % %% Signal samples
% % feature_chosen(channel,:)=trial_data;
% %
% % elseif feature==35
% % %% Within-trial correlation
% % numLags=size(trial_data,2)-1;
% % [acf,lags,~] =autocorr(trial_data,numLags);
% % feature_chosen(channel,:)= acf(2:end);
% % end
% end
% end
% end
% [Patient trial t]
% end
% end
% if strcmp(ictal_or_inter,'ictal')
% save([Patient_initials,'_ds4100_Features_Seizure',num2str(trial),chars,'.mat'],'features','channels_id',...
% 'pre_siz_analysis','post_siz_analysis','window_spans','step_size','featuress',...
% 'channels','windows','Time_windows','channel_info')
% elseif strcmp(ictal_or_inter,'interictal')
% save([Patient_initials,'_ds4100_Features_Interictal',num2str(trial),chars,'.mat'],'features','channels_id',...
% 'pre_siz_analysis','post_siz_analysis','window_spans','step_size','featuress',...
% 'channels','windows','Time_windows','channel_info')
% end
% toc
end
end