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Behavior Analysis.txt

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+%% Data analysis of participants in Guangzhou (gz)
+clear
+clc
+emerge=cell(29,3);
+for n=1:29
+     load('data_path');
+     j=1;
+     k=1;
+    for i=2:161
+        if strcmp(stor_var{i,5},'CORRECT')==1
+            j=j+1;
+       else
+           k=k+1;
+       end
+     end
+   emerge1={};
+   emerge2=[];
+   a=1;
+   b=1;
+   c=1;
+   if j>k
+      for i=2:161
+          if (strcmp(stor_var{i,4},'TIMEOUT') == 0) && (strcmp(stor_var{i,5},'CORRECT') == 1) 
+               emerge1{a,1}=stor_var{i,6};
+               a=a+1;
+          end
+      end
+  else
+     for i=2:161
+          if (strcmp(stor_var{i,4},'TIMEOUT') == 0) && (strcmp(stor_var{i,5},'WRONG') == 1) 
+               emerge1{a,1}=stor_var{i,6};
+               a=a+1;
+          end
+      end
+   end    
+   emerge1=cell2mat(emerge1);
+   for i=1:length(emerge1)
+       if (emerge1(i,1)>=(mean(emerge1(:,1))-3.*std(emerge1(:,1)))) && (emerge1(i,1)<=(mean(emerge1(:,1))+3.*std(emerge1(:,1))))
+           emerge2(b,1)=emerge1(i,1); 
+           b=b+1;
+       end
+   end
+   emerge{n,1}=n;
+   emerge{n,2}=length(emerge1)./160;
+   emerge{n,3}=median(emerge2(:,1));
+end
+savepath = strcat('filesave_path');
+save(savepath,'emerge'); 

Binary Analysis.txt

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+ clc;
+clear;
+main_path = 'D:\paper2\paper3\心脏周期（代码）';
+rr_path = 'RR';
+mark_path = 'mark';
+behav_path = 'behavior';
+ecg_path = 'amp_clean';
+r_tpath = 'R_T';
+file = {'guangzhou','xizang'};
+  fs = 200;
+ for i = 1:length(file)
+     filert = dir(fullfile(main_path,r_tpath,file{i}));
+     for j = 3:length(filert)
+         sys_dys=[];
+%          rt = load(fullfile(main_path,r_tpath,file{i},filert(j).name));
+         rt = load(fullfile(main_path,r_tpath,'xizang',filert(j).name));
+         rt = rt(:,1)-39;%time delay 39 sampling
+%          rt = rt(:,1)*200*60-39;%time delay 39 sampling
+         sub = filert(j).name(1:length(filert(j).name)-4);
+         mark = xlsread(fullfile(main_path,mark_path,file{i},[sub,'.xlsx']));
+         data_delay = load(fullfile(main_path,ecg_path,file{i},filert(j).name));
+         data = data_delay(1:size(data_delay,1));
+         behave = load(fullfile(main_path,behav_path,file{i},['stor_var',sub,'.mat']));
+         for h = 1:size(mark,1)/2        
+            R_near = max(find(rt(:,1)/(60*fs)<(mark(h*2-1,1))));
+            ecgpos1 =  rt(R_near,1);
+            RRint = rt(R_near+1)-rt(R_near);
+            plot(rt(R_near):rt(R_near+1),data(rt(R_near):rt(R_near+1)),'k')
+            ecgpos2 = ecgpos1+RRint-12;
+            %twave = data(ecgpos1+28:ecgpos2,:);          
+            twave = data(ecgpos1+28:ecgpos2,:);  
+%              hold on
+%              plot(ecgpos1+28:ecgpos2,twave,'r')
+             if (RRint-28)/3 <= size(twave,1) % from xizang 4.mat start
+             tmaxpos = find(twave(1:((RRint-28)/3),1)==max(twave(1:((RRint-28)/3),1)));
+             c=0;
+             else
+             tmaxpos = find(twave(1:size(twave,1),1)==max(twave(1:size(twave,1),1)));  
+             c=1;
+             end
+            twave2 = twave(tmaxpos:size(twave,1),:);
+              hold on
+              plot(ecgpos1+28+tmaxpos-1:ecgpos2,twave2,'g')
+            dp = 0.12*fs;
+            if dp > size(twave,1)
+                xm = find(diff(twave2(:,1))==min(diff(twave2(:,1))));
+            else
+                if dp < length(twave2)
+                xm = find(diff(twave2(1:dp,1))==min(diff(twave2(1:dp,1))));
+                a=0;
+                else
+                xm = find(diff(twave2(1:length(twave2),1))==min(diff(twave2(1:length(twave2),1))));
+                a=1;
+                end
+            end
+            ym = twave2(xm,1);
+            xr = 16+xm;          
+            if xr > length(twave2)
+                xr = length(twave2);
+                b=0;
+            else
+                xr = xr;
+                b=1;
+            end
+            xseq = xm:xr;
+            yseq = twave2(xm:xr,1);
+            x_tend = trapez_area(xm, ym, xseq, yseq, xr);
+            nextR=rt(R_near+1)-0.8;
+            syslen = 28+tmaxpos+x_tend;
+            sys_dp = ecgpos1:ecgpos1+syslen-1;
+            hold on 
+            plot(ecgpos1:ecgpos1+28+tmaxpos+x_tend,data(ecgpos1:ecgpos1+28+tmaxpos+x_tend),'b')
+            hold on 
+            plot(rt(R_near+1)-syslen:rt(R_near+1),data(rt(R_near+1)-syslen:rt(R_near+1)),'r')
+            dias_dp = (nextR-syslen):(nextR-0.08);
+            %diastole_ecg=data(diastole_dp,:);
+            if (mark(h*2-1,1))*60*fs <= ecgpos1+syslen-1
+               sys_dys(h,1) = behave.stor_var{h+1,6};
+               sys_dys(h,2) = 1;
+            else
+               if ecgpos1+syslen-1 < nextR-syslen && (mark(h*2-1,1))*60*fs < nextR-syslen
+               sys_dys(h,1) = behave.stor_var{h+1,6};
+               sys_dys(h,2) = 2;
+               elseif ecgpos1+syslen-1 >= nextR-syslen || (mark(h*2-1,1))*60*fs >= nextR-syslen
+               sys_dys(h,1) = behave.stor_var{h+1,6};
+               sys_dys(h,2) = 3;
+               end         
+            end 
+            sys_dys(h,3)= syslen/RRint;
+            sys_dys(h,4)= syslen;
+            sys_dys(h,5)= RRint;  
+            sys_dys(h,6)= a;
+            sys_dys(h,7)= b;
+            sys_dys(h,8)= c;    
+         end
+         savepath=fullfile(main_path,'cardic2',[sub,'.mat']);
+         save(savepath,'sys_dys')
+    end
+ end

Data Modeling.txt

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+import pandas as pd
+import numpy as np
+import hddm
+from scipy import stats
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pymc
+import kabuki
+
+data = hddm.load_csv('rtgz.csv')
+#data = hddm.utils.flip_errors(data)
+fig = plt.figure()
+ax = fig.add_subplot(111, xlabel='RT', ylabel='count', title='RT distributions')
+for i, subj_data in data.groupby('subj_idx'):
+    subj_data.rt.hist(bins=20, histtype='step', ax=ax)
+
+datana=data.dropna(axis=0,how='any')
+m_exp = hddm.HDDM(datana)
+m_exp.find_starting_values()
+m_exp.sample(10000, burn=1000)
+
+stats_exp = m_exp.gen_stats()
+stats_exp.to_csv('stats_gz.csv') 

Pre- and Post- Stimulus Heart Rate Analysis.txt

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+clear;
+clc;
+mainfile = 'mainfile_path';
+file = dir(fullfile(mainfile,'qrs_path'));
+filevent = dir(fullfile(mainfile,'rt_path'));
+
+for i = 1:length(file)
+    rr=[];
+    a = 1;
+    data = load(fullfile(mainfile,'qrs_data_path'));
+    sub = file(i,1).name(1:length(file(i,1).name)-4);
+    load([mainfile,'rt_data_path']);
+    load([mainfile,'event_data_path']);
+    lung = length(trg);
+    d = 1;
+    e = 1;
+    for h = 2:size(stor_var,1)
+    if  strcmp(stor_var{h,5},'CORRECT')==1
+        d=d+1;
+    elseif strcmp(stor_var{h,5},'WRONG')==1
+        e=e+1;
+    end
+    end
+    b = 1;
+    for j = 2:length(stor_var)
+        if d > e && strcmp(stor_var{j,5},'CORRECT')==1
+            trg(b,2)= stor_var{j,6};
+            b= b+1;
+        elseif d <= e && strcmp(stor_var{j,5},'WRONG')==1
+            trg(b,2)= stor_var{j,6};
+            b= b+1;
+        end
+    end
+    for h = 1:lung        
+         R_near = max(find(data(:,1)<trg(h,1)));
+         if (R_near+4) < length(data) && 0 <(R_near-3)
+              rr(a,1) = data(R_near-2)-data(R_near-3);
+              rr(a,2) = data(R_near-1)-data(R_near-2);
+              rr(a,3) = data(R_near)-data(R_near-1);
+              rr(a,4) = data(R_near+1)-data(R_near);
+              rr(a,5) = data(R_near+2)-data(R_near+1);
+              rr(a,6) = data(R_near+3)-data(R_near+2);
+              rr(a,7) = trg(h,2);
+              rr(a,8) = log10(trg(h,2));
+              rr(a,9) = str2num(sub);
+              a = a+1;
+        end
+    end
+    rrm(i-2,1) = mean(rr(:,1));
+    rrm(i-2,2) = mean(rr(:,2));
+    rrm(i-2,3) = mean(rr(:,3));
+    rrm(i-2,4) = mean(rr(:,4));
+    rrm(i-2,5) = mean(rr(:,5));
+    rrm(i-2,6) = mean(rr(:,6));
+    rrm(i-2,7) = str2num(sub);
+end

diff2.txt

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+function outp2=diff2(vec)
+for i = 2:size(vec,1)
+    outp2(i-1,1)= vec(i,1)-vec(i-1);
+end
+end