--- a
+++ b/convert_IIC_perhour.m
@@ -0,0 +1,106 @@
+% 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');