% Wei-Long Zheng, MGH

% Email: weilonglive@gmail.com

clear

feature_hour_folder = '\Features\YNH\';

feature_folder = '\Features_CNN_IIIC\YNH\';

save_folder = '\';

pts_info = readtable('ICARE_CaseData_Final_20181108.csv');

list = dir([feature_folder,'*.mat']);

isfile=~[list.isdir]; 

featurenames={list(isfile).name};

num_file = length(featurenames);

filenames = featurenames;

max_hour = 0;

for i = 1:num_file

    underlineLocations = find(filenames{i} == '_');

    pt_name{i} = filenames{i}(1:underlineLocations(1)-1);

    load ([feature_hour_folder,filenames{i}],'eeg_hour');

    if ~isempty(eeg_hour)

    if max_hour<eeg_hour{end,1}

        max_hour = eeg_hour{end,1};

    end

    end

end

[unique_names, idx ,idx2] = uniquecell(pt_name);

num_pts = length(unique_names);

feature_all = NaN(num_pts,max_hour);

score_all = NaN(num_pts,max_hour);

cpc_scores = zeros(num_pts,1);

features = cell(1,6);

feature_score = cell(1,6);

for ifea = 1:6

    features{ifea} = feature_all;

    feature_score{ifea} = score_all;

end

select_pts = table();

for i = 1:num_pts

    current_pts = unique_names{i};

    indx = ~cellfun(@isempty, strfind(pts_info.sid,current_pts));

    indx_tmp = find(indx==true);

    bestCpcBy6Mo = pts_info.bestCpcBy6Mo(indx_tmp(1));

    cpc_scores(i) = bestCpcBy6Mo;

    unique_names_cpc{i} = [unique_names{i},'(',num2str(bestCpcBy6Mo),')'];

    file_index = find(idx2==i);

        for j = 1:length(file_index)

            load([feature_hour_folder,filenames{file_index(j)}],'eeg_hour');

            load([feature_folder,filenames{file_index(j)}],'prediction');

            load(['Z:\Projects\Weilong\Cardiac_arrest_EEG\Features\YNH\',filenames{file_index(j)}],'eeg_masks')

            col_num_count = 0;

            for k = 1:size(eeg_hour,1)

                if ~isempty(eeg_hour{k,1})

                index = cellfun(@(x)isequal(x,eeg_hour{k,1}),eeg_hour);

                [row,col] = find(index);

                current_mask = eeg_masks(k,col);

%                 current_feature = eeg_feature(k,col);

                weight_clean = zeros(1,length(col));

                feature_raw = zeros(1,length(col));

                end_index = min((col_num_count+length(col))*30,size(prediction,1));

                current_feature = prediction(col_num_count*30+1:end_index,:); % convert 10s to 5min

                col_num_count = col_num_count+length(col);

                for ifeature = 1:6

                for m = 1:length(col)

%                     if ~isempty(current_feature{m})

                    indx = ~cellfun(@isempty, strfind(string(current_mask{m}),'normal'));

                    weight_clean(m) = sum(indx)/length(indx);

    %                 feature_raw(m) = mean(current_feature{m}(5:13:end));

                    end_tmp = min(m*30,size(current_feature,1));

                    feature_raw(m) = mean(current_feature((m-1)*30+1:end_tmp,ifeature));

    %                 feature_raw(m) = mean(mean(current_feature{m})); %burst suppression ratio

%                     end

                end

%                 if sum(feature_raw)~=0

                weight_clean_norm = weight_clean/sum(weight_clean);

                feature_clean = sum(weight_clean_norm.*feature_raw);

    %             if max(weight_clean)<0.5

    %                 feature_all(i,eeg_hour{k,1}) = NaN;

    %             else

    %                 feature_all(i,eeg_hour{k,1}) = feature_clean;

    %             end

                features{ifeature}(i,eeg_hour{k,1}) = feature_clean;

                feature_score{ifeature}(i,eeg_hour{k,1}) = max(weight_clean);

%                 end

                end

                end

            end

        end

end

%     features{ifeature} = feature_all;

%     feature_score{ifeature} = score_all;

% end

save([save_folder,'ynh_iic_pattern'],'features','feature_score','cpc_scores','unique_names','unique_names_cpc','-v7.3');

% writetable(select_pts,'ynh_pts.csv');