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Detection_parallelized_LowRes_vF.m
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Detection_parallelized_LowRes_vF.m
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function [] = Detection_parallelized_LowRes_vF(Sub_ID,ss_start,ss_End,LR)
%% Function inputs
% Sub_ID: Patient ID
% ss_start, ss_end: Session Range for Processing
% LR: 'L' or 'R' representing craniectomy hemisphere
% clear all
% close all
% clc
%%
% Main SD detection code for WAVEFRONT, along with performance calculations
%
% See: README.txt and [1] for more info.
% [1] Alireza Chamanzar, Jonathan Elmer, Lori Shutter, Jed Hartings, Pulkit Grover,
% "Noninvasive and reliable automated detection of spreading depolarization in severe traumatic brain injury using scalp EEG",
% Submitted to Nature Comms Med, 2022.
% Author: Alireza Date: 2022/01/05 12:00:00 Revision: 0.1
% Copyright: This project is licensed - see the LICENSE.md file for details
% Sub_ID = '04-1201';
% LR='R';
% ss_start = 1;
% ss_End = 1;
% im_scale = 0.1;
%% Initialization of Parameters:
crani_ind = 1:19;
if(LR=='L')
LR_sgn = -1;
crani_ind = crani_ind([1,3,4,6,8,10,11,13,15,17,18]);
else
LR_sgn = 1;
crani_ind([1,4,6,8,11,13,15,18]) = [];
end
im_scale = 0.0945;
DELTA_T = 60;
% Grid search for set of parameters in WAVEFRONT:
% THR_1 = 0.05:0.05:0.8;%mm
% THR_3 = linspace(0.1,0.9,20);
% % THR_1 = THR_1([1,2,3,4,5,6,7,8,9]);%mm
% THR_2 = 0.6:0.1:0.8;
% % THR_3 = THR_3(10:15);
% THR_4 = 2:10;
% OPTIMAL set of parameters (for quick run through the code):
THR_1 = 0.300000000000000 ;
THR_2 = 0.600000000000000;
THR_3 = 0.689473684210526;
THR_4 = 2.0000;
cd(['D:\SD-II_POST_ICA\SD-II\',Sub_ID,'\',Sub_ID])
Session_names = dir('Patient*');
Sessions_size = size(Session_names,1);
TPR_num = 0;
TPR_denum = 0;
TNR_num = 0;
TNR_denum = 0;
Tot_detectable_CSD = 0;
Detected_CSD = 0;
CSD_event_quality_tot = [];
% Beginning of Session-Specific Loop
for ss = ss_start:ss_End
cd(Session_names(ss).name)
current_path = pwd;
EEG_vis = pop_loadset('filename',[Session_names(ss).name,'_Visualization_Delta.set'],'filepath',current_path);
% Filter unnecessary events
event_delete_ind = [];
for event_ind=1:size(EEG_vis.event,2)
test_ind = strfind(string(EEG_vis.event(event_ind).type),'Data Quality');
test_ind = cat(1,test_ind,strfind(string(EEG_vis.event(event_ind).type),'Detection'));
test_ind = cat(1,test_ind,strfind(string(EEG_vis.event(event_ind).type),'boundary'));
test_ind = cat(1,test_ind,strfind(string(EEG_vis.event(event_ind).type),'Electrode'));
target_detected = 0;
for ss_ind = 1:size(test_ind,1)
if(size(test_ind,1)>1)
if(~isempty(test_ind{ss_ind,1}))
target_detected = 1;
end
else
if(~isempty(test_ind))
target_detected = 1;
end
end
end
if(target_detected==1)
event_delete_ind = cat(1,event_delete_ind,event_ind);
continue;
end
end
EEG_vis = pop_editeventvals(EEG_vis,'delete',event_delete_ind);
[ind_r,ind_c] = find(isnan(EEG_vis.data));
EEG_vis.data(ind_r,ind_c) = 0;
% Load Global Mean data (Session-wide average of EEG_PW)
Global_M = load([Session_names(ss).name ,'GlobalMean_Delta_SDII_vConfusion_240minOv180min_CCWL20min_Xcrr_Improved_Pruned_vVII.mat']);
Global_M = Global_M.Global_M;
Part_names = dir([Session_names(ss).name,'_CSD_ind_*_Delta_SDII_vConfusion_240minOv180min_CCWL20min_Xcrr_Pruned_vVII.mat']);
Part_size = size(Part_names,1);
Part_name_char = string({Part_names.name});
part_ind = strfind(Part_name_char,'ind');
of_ind = strfind(Part_name_char,'Delta');
Part_num = [];
if(Part_size>1)
for i=1:Part_size
Part_num = cat(1,Part_num,str2double(Part_name_char{i}(part_ind{i}+4:of_ind{i}-2)));
end
else
Part_num = str2double(Part_name_char{1}(part_ind+4:of_ind-2));
end
[~,Part_ind] = sort(Part_num);
% Load EEG Electrode Spatial Locations
UPMC_TBI_EEG_Loc = load('D:\SilenceMap-v1.0\Chamanzar-SilenceMap-668f8ab\EEG_prep\UPMC_TBI_EEG_Loc_SDII_ordered.mat');
UPMC_TBI_EEG_Loc = UPMC_TBI_EEG_Loc.UPMC_TBI_EEG_Loc;
BCNT_tot = {};
CNT_tot = {};
CSD_GT_tot = {};
T_start_tot = {};
T_end_tot = {};
Events_tot = {};
CSD_type_tot = {};
Events_type_tot = {};
num_good_elec_tot = {};
CSD_GT_total = {};
CSD_Labels_total = {};
Strt_tot = {};
Endd_tot = {};
T_start_tot_temp = {};
T_end_tot_temp = {};
BCNT_tot_temp = {};
CNT_tot_temp = {};
CSD_GT_tot_temp = {};
num_good_elec_tot_temp = {};
CSD_type_tot_tempp = {};
Endd_tot_temp = {};
Strt_tot_temp = {};
Thr_counter_tot = {};
%% Beginning of Part-Specific Loop
for CSD_ind_orig = 1:Part_size
T_start_tot_temp_temp = {};
T_end_tot_temp_temp = {};
BCNT_tot_temp_temp = {};
CNT_tot_temp_temp = {};
CSD_GT_tot_temp_temp = {};
num_good_elec_tot_temp_temp = {};
CSD_type_tot_temp_temp = {};
Endd_tot_temp_temp = {};
Average_Vel_tot_temp_temp = {};
Strt_tot_temp_temp = {};
Thr_counter_tot_temp = {};
fprintf(['Starting ' sprintf('CSD_ind_%d',CSD_ind_orig) '\n'])
Name = Session_names(ss).name;
load_temp = load(Part_names(Part_ind(CSD_ind_orig)).name)
CSD_GT = load_temp.CSD_GT;
CSD_GT_total{CSD_ind_orig} = load_temp.CSD_GT_total;
CSD_Labels_total{CSD_ind_orig} = load_temp.CSD_Labels_total;
CSD_type = load_temp.CSD_type;
EEG_PW = load_temp.EEG_PW;
EEG_mask = load_temp.EEG_mask;
Events = load_temp.Events;
Events_type = load_temp.Events_type;
T_end = load_temp.T_end
T_start = load_temp.T_start
srate = load_temp.srate;
%% Spatial projection on a 2D plane:
sensor_locs_n = UPMC_TBI_EEG_Loc(1:size(UPMC_TBI_EEG_Loc,1)-1,1:3)/100;
%Reorder the electrodes based on SD-II dataset:
[~,sensor_ind] = sort(UPMC_TBI_EEG_Loc(1:size(UPMC_TBI_EEG_Loc,1)-1,4),'ascend');
sensor_locs_n = sensor_locs_n(sensor_ind,:);
% Get Part specific event info including Event type and timestamp
if(isempty(CSD_type))
CSD_type = {'1_No CSD'};
CSD_GT(1) = 1;
end
CSD_ind = Part_num(Part_ind(CSD_ind_orig));
Sub_sampl = 30*srate;
EEG_PW(EEG_PW<0) = 0;
EEG_PW = EEG_PW(:,1:Sub_sampl:end);
EEG_mask = EEG_mask(:,1:Sub_sampl:end);
CSD_GT_ind = find(CSD_GT);
CSD_GT_ind = round(CSD_GT_ind/Sub_sampl);
CSD_GT_ind(CSD_GT_ind==0) = 1;
CSD_GT = zeros(1,size(EEG_PW,2));
CSD_GT(CSD_GT_ind) = 1;
CSD_GT_temp = CSD_GT;
%%
Normal_events_ind = (strcmp(Events_type,'Data Quality Normal'));
Events_ind = find(Events);
Events(Events_ind(Normal_events_ind)) = 0;
Events_type((Normal_events_ind)) = [];
Events_ind = find(Events);
Events_ind = round(Events_ind/Sub_sampl);
Events = zeros(1,size(EEG_PW,2));
for event_id=1:size(Events_ind,2)
if(Events_ind(event_id)==0)
Events_ind(event_id)=1;
end
temp = Events_ind(event_id);
while(Events(temp)==1 && temp<size(Events,2))
temp = temp+1;
end
Events(temp) = 1;
end
Events_temp = Events;
% Calculate spatial loc of electrodes in cart. and spherical coords
Alphax = 0;%*(rand(1)-0.5)+n*0;
Alphay = 0;%80*(rand(1)-0.5)+n*0;
Alphaz = 90;
Rx = [1 0 0; 0 cos((pi/180)*Alphax) -sin((pi/180)*Alphax); 0 sin((pi/180)*Alphax) cos((pi/180)*Alphax)];
Ry = [cos((pi/180)*Alphay) 0 sin((pi/180)*Alphay); 0 1 0; -sin((pi/180)*Alphay) 0 cos((pi/180)*Alphay)];
Rz = [cos((pi/180)*Alphaz) -sin((pi/180)*Alphaz) 0; sin((pi/180)*Alphaz) cos((pi/180)*Alphaz) 0; 0 0 1];
Rtot = Rz * Ry * Rx;
sensor_locs_n = (Rtot*sensor_locs_n')';
[phi,theta,r]=cart2sph(sensor_locs_n(:, 1), sensor_locs_n(:, 2), sensor_locs_n(:, 3));
phi((phi<0))=phi((phi<0))+2*pi;
theta((theta>=0))=((pi/2)-theta((theta>=0)));
theta((theta<0))=(-1)*(theta((theta<0))-(pi/2));
theta=round((180/pi)*theta);
phi=round((180/pi)*phi);
phi((phi==0))= phi((phi==0))+1;
phi((phi==360))= 1;
theta((theta==0))= theta((theta==0))+1;
% Project Data into 2D space using above spatial conversions
EEG_median = (median(EEG_PW(crani_ind,:),1));
I_EEG = ones(180,359,size(EEG_PW,2));
I_mask = zeros(180,359,size(EEG_mask,2));
for k=1:size(EEG_PW,2)
I_EEG(:,:,k) = I_EEG(:,:,k)*EEG_median(1,k);
for i=1:size(EEG_PW,1)
if(LR_sgn==1)
if(theta(i)>0 && theta(i)<=180 && phi(i)>0 && phi(i)<185)
phi_temp = phi(i);
if(phi(i)==1 && theta(i)<20)
phi_temp = 90;
end
I_EEG(theta(i),phi_temp,k) = EEG_PW(i,k);
I_mask(theta(i),phi_temp,k) = EEG_mask(i,k);
end
else
if(theta(i)>0 && theta(i)<=180 && (phi(i)>175 || phi(i)<5) && phi(i)<360)
phi_temp = phi(i);
if(phi(i)==1 && theta(i)<20)
phi_temp = 90;
end
if(phi(i)>175)
phi_temp = 180 - (phi(i)-180);
end
I_EEG(theta(i),phi_temp,k) = EEG_PW(i,k);
I_mask(theta(i),phi_temp,k) = EEG_mask(i,k);
end
end
end
end
%% EEG spatial interpolation:
% Smooth 'Sparse' spatial projections, creating 'I_smoothed'
%Gaussian:
figure;mesh(I_EEG(:,:,309))
h = ones(30);
for k=1:size(I_EEG,3)
I_EEG(:,:,k) = imgaussfilt(I_EEG(:,:,k),20,'Padding',EEG_median(1,k));%resample(imgaussfilt(temp,10,'Padding',0),size(I_EEG,1),size(temp,1),5);
I_mask(:,:,k) = imgaussfilt( I_mask(:,:,k),20,'Padding',0);%resample(imgaussfilt(temp,10,'Padding',0),size(I_mask,1),size(temp,1),5);
end
I_mask = double(I_mask>0);
figure;mesh(I_EEG(:,:,309))
I_EEG = I_EEG(1:100,:,:);
%
%% Temporal subsampling of frames II:
Thr_counter = 0;
Thr_ind = 0;
% Begin Parameter-specific loops, starting with Thr_3
for Thr_3_ind = 1:size(THR_3,2)
Thr_3 = THR_3(Thr_3_ind)
II = I_EEG(end:-1:1,:,:);
num_good_elec = sum(EEG_mask(crani_ind,:),1);
II_median = (median(reshape(II,[],1,size(II,3)),1));
% Thresholding the frames to obtain binary images (I_BW):
Thr = Thr_3;
hh = max(max(II));
ll = min(min(II));
temp = II;
II(temp<=II_median+Thr*(hh-II_median)) = 0;
II(temp>II_median+Thr*(hh-II_median)) = 255;
II(:,:,num_good_elec<5)=0;
CC_BW = squeeze(sum(sum(II,1),2))';
II(:,:,CC_BW>=0.2*255*size(II,1)*size(II,2))=0;
II_resize = [];
for t=1:size(II,3)
II_resize = cat(3,II_resize,imresize(squeeze(II(:,:,t)),im_scale));
end
II = II_resize;
value = II;
activity = (II(end:-1:1,:,:));
%% Calculating Optical flows:
Ttotal = size(activity,3);
%Orange color
C = [1 .5 0];
Stp = 1;
disp('Computing Optical Flow');
% Compute optical flow
opticalFlow = opticalFlowHS;
of = estimateFlow(opticalFlow, value(:,:,1));
% the variable 'of' below stores optical flows at each
% x,y as real and imaginary values.
bb = {};
cc = {};
for t = 1:Ttotal-Stp
of(t)=estimateFlow(opticalFlow, value(:,:,t+Stp));
label = bwlabel(activity(:,:,t));
st = regionprops( label,'centroid', 'Area', 'BoundingBox' );
bb{t} = round(cat(1,st.BoundingBox)); % Bounding box for connected components
cc{t} = round(cat(1,st.Centroid)); % Centroid for connected components
end
%% Calculating the histogram of orientations for all BBoxes:
vel_x = zeros(size(II,1),size(II,2),size(of,2));
vel_y = zeros(size(II,1),size(II,2),size(of,2));
Theta_scale = 100/(size(II,1)-1);
Phi_scale = 359/(size(II,2)-1);
for i=1:size(of,2)
vel_x(:,:,i) = of(i).Vx(end:-1:1,:).*cos((pi/180)*repmat(linspace(90,-10,size(II,1))',1,size(II,2)))*75*Phi_scale*(pi/180);
vel_y(:,:,i) = of(i).Vy(end:-1:1,:)*75*Theta_scale*(pi/180);
end
% Old code for plotting optical flows
% Fr=[];
%
% for r=309
% figure;
% % r = 225;%t=15min;
% set(gcf, 'Position', get(0, 'Screensize'));
% imshow(value(end:-1:1,:,r),'InitialMagnification','fit');
% hold on
% %ploting BBoxs:
% for i = 1:size(bb{r},1)
% rectangle('Position',bb{r}(i,:),'EdgeColor','r','LineWidth',4)
% end
% quiver(of(r).Vx(end:-1:1,:).*cos((pi/180)*repmat(linspace(90,-10,size(II,1))',1,size(II,2))),(-of(r).Vy(end:-1:1,:)),1,'color',C,'LineWidth',2)
% Fr = cat(1,Fr,getframe(gcf));
% hold off
% end
% Calculate magnitude and orientation of optical flows
velocity = {};
for t =1:size(vel_x,3)
if isempty(bb{t})
velocity{t,1} = [];
continue;
end
for i = 1:size(bb{t},1)
if((bb{t}(i,3)* bb{t}(i,4))<1) % Checking area of bounding box is >4
continue;
end
orient = atan2d(vel_y(bb{t}(i,2):bb{t}(i,2)+bb{t}(i,4)-1,bb{t}(i,1):bb{t}(i,1)+bb{t}(i,3)-1,t),...
vel_x(bb{t}(i,2):bb{t}(i,2)+bb{t}(i,4)-1,bb{t}(i,1):bb{t}(i,1)+bb{t}(i,3)-1,t));
mag = (vel_y(bb{t}(i,2):bb{t}(i,2)+bb{t}(i,4)-1,bb{t}(i,1):bb{t}(i,1)+bb{t}(i,3)-1,t).^2+...
vel_x(bb{t}(i,2):bb{t}(i,2)+bb{t}(i,4)-1,bb{t}(i,1):bb{t}(i,1)+bb{t}(i,3)-1,t).^2).^0.5;
orient(orient<0) = 360 + orient(orient<0);
orient(mag==0) = -1;
[Hof,idx] = histcounts(reshape(orient,1,[]),[linspace(0,360,9)-22.5,360+22.5]);
Mag = mag;
velocity{t,i} = struct(...
'bbox',bb{t}(i,:),...
'cc',cc{t}(i,:),...
'mag',Mag, ...
'hof',Hof,...
'idx',idx,...
'angle',orient);
end
end
%% Histogram @ 10min(86):
% % Old code for plotting histogram of BBoxs
% r=309;% hidtogram of BBox @ 10 min
% figure;polarhistogram('BinEdges',(pi/180)*[linspace(0,360,9)-22.5,360+22.5],'BinCounts',velocity{r, 1}.hof)
%
%% Quantization of orientations and creating OBBox:
velocity_tot = velocity;
% Begin parameter 'Thr_1' specific-loop
for Thr_1_ind = 1:size(THR_1,2)
Thr_1 = THR_1(Thr_1_ind)
velocity = velocity_tot;
Fr = [];
VX = zeros(size(II,1),size(II,2),size(velocity,1));
VY = zeros(size(II,1),size(II,2),size(velocity,1));
SCR_tot = zeros(size(II,1),size(II,2),size(velocity,1));
ANgle = zeros(size(II,1),size(II,2),size(velocity,1));
Bin_Cntr = linspace(0,360,9);
Bin_Cntr(end) = [];
bbb = {};
ccc = {};
BB = {};
for t=1:size(velocity,1)
Vx = zeros(size(vel_x,1),size(vel_x,2),size(velocity,2));
Vy = zeros(size(vel_y,1),size(vel_y,2),size(velocity,2));
scr_tot = zeros(size(vel_x,1),size(vel_x,2),size(velocity,2));
Angls = -1*ones(size(Vx,1),size(Vx,2),size(velocity,2));
for i=1:size(velocity,2)
if isempty(velocity{t,i})
BB{t,1} = [];
continue;
end
vel = velocity{t,i}.angle;
vel(vel==-1) = [];
mag = velocity{t,i}.mag;
mag(mag==0) = [];
% Score BBox's based upon maag and orietnation
% compare to Thr_1 to remove pop-ups and fades
scr = sqrt(mean(mean(mag.*cos(vel*pi/180))).^2 + mean(mean(mag.*sin(vel*pi/180))).^2);
if(isempty(vel))
scr = 0;
end
if(scr<Thr_1)
velocity{t,i}.angle = -1 * ones(size(velocity{t,i}.angle));
end
Hof=idx(velocity{t,i}.hof>=0.5*max(velocity{t,i}.hof));
velocity{t,i}.hof=Hof;
orient=velocity{t,i}.angle;
Orient = -1*ones(size(orient));
for j=1:size(Hof,2)
Orient(orient>=Hof(j) & orient<(Hof(j)+45))=Hof(j)+22.5;%%%%Central point of the bin
end
Orient(Orient==360) = 0;
velocity{t,i}.angle=Orient;
mag = velocity{t,i}.mag;
mag(Orient==-1)=0;
velocity{t,i}.mag = mag;
temp = zeros(size(vel_x,1),size(vel_x,2));
temp(velocity{t,i}.bbox(2):velocity{t,i}.bbox(2)+velocity{t,i}.bbox(4)-1,velocity{t,i}.bbox(1):velocity{t,i}.bbox(1)+velocity{t,i}.bbox(3)-1)=(mag.*cos((pi/180)*Orient));
Vx(:,:,i) = temp;
temp = zeros(size(vel_y,1),size(vel_y,2));
temp(velocity{t,i}.bbox(2):velocity{t,i}.bbox(2)+velocity{t,i}.bbox(4)-1,velocity{t,i}.bbox(1):velocity{t,i}.bbox(1)+velocity{t,i}.bbox(3)-1)=(mag.*sin((pi/180)*Orient));
Vy(:,:,i) = temp;
temp_Angle = -1*ones(size(vel_x,1),size(vel_x,2));
temp_Angle(velocity{t,i}.bbox(2):velocity{t,i}.bbox(2)+velocity{t,i}.bbox(4)-1,velocity{t,i}.bbox(1):velocity{t,i}.bbox(1)+velocity{t,i}.bbox(3)-1)=(Orient);
Angls(:,:,i) = temp_Angle;
scr_tot(velocity{t,i}.bbox(2):velocity{t,i}.bbox(2)+velocity{t,i}.bbox(4)-1,velocity{t,i}.bbox(1):velocity{t,i}.bbox(1)+velocity{t,i}.bbox(3)-1,i) = scr;
end
for i=1:size(velocity,2)
[i_r,i_c] = find(Angls(:,:,i)~=-1);
Angls(i_r,i_c,1) = Angls(i_r,i_c,i);
end
Angls = Angls(:,:,1);
Vx = sum(Vx,3);
Vy = sum(Vy,3);
scr_tot = sum(scr_tot,3);
%Creating the OBBoxes (Oriented Bounding Boxes):
ANgle(:,:,t) = Angls;
VX(:,:,t) = Vx;
VY(:,:,t) = Vy;
SCR_tot(:,:,t) = scr_tot;
C=unique(Angls);
C(C==-1)=[];
if (size(C,2)>0)
for q=1:size(C,1)
temp=Angls;
temp(temp~=C(q))=0;
temp(temp~=0)=255;
label(:,:,t)=bwlabel(temp);
st = regionprops( label(:,:,t),'centroid', 'BoundingBox' );
bbb{t} = round(cat(1,st.BoundingBox)); % Bounding box for connected components of Orient
ccc{t} = round(cat(1,st.Centroid)); % Bounding box for connected components of Orient
if (isempty(bbb{t})==0)
p=0;
for o = 1:size(bbb{t},1)
if(bbb{t}(o,3)*bbb{t}(o,4)>1)
p=p+1;
BB{t,find(Bin_Cntr==C(q))}(p,:) = cat(2,bbb{t}(o,:),C(q),ccc{t}(o,:));
end
end
end
end
end
end
if(size(BB,1)<size(velocity,1))
for t=size(BB,1)+1:size(velocity,1)
for dum=1:size(BB,2)
BB{t,dum}=[];
end
end
end
%% Scoring the OBBox based on consistency in orientation & speed:
% Begin 'Thr_2' specific-loop
for Thr_2_ind = 1:size(THR_2,2)
% Begin 'Thr_4' specific-loop
for Thr_4_ind = 1:size(THR_4,2)
Thr_2 = THR_2(Thr_2_ind)
Thr_4 = THR_4(Thr_4_ind)
Threshld_Frame=Thr_2;%0.1:0.2:0.9
SCR = BB;
Vel_SCR = BB;
L_wind = floor(Thr_4*(60*srate/(Sub_sampl)));%temporal neighborhood 2min
Gap_q = floor(0*(60*srate/(Sub_sampl)));%temporal neighborhood 2min
for t=L_wind+1:size(BB,1)-L_wind
for i=1:size(BB,2)
SCR{t,i} = zeros(size(SCR{t,i},1),1);
Vel_SCR{t,i} = zeros(size(Vel_SCR{t,i},1),1);
if (isempty(BB{t,i}))
continue;
end
for j=1:size(BB{t,i},1)
VV = mean(mean(sqrt((VX(BB{t,i}(j,2):BB{t,i}(j,2)+BB{t,i}(j,4)-1,BB{t,i}(j,1):BB{t,i}(j,1)+BB{t,i}(j,3)-1,t)).^2 ...
+(VY(BB{t,i}(j,2):BB{t,i}(j,2)+BB{t,i}(j,4)-1,BB{t,i}(j,1):BB{t,i}(j,1)+BB{t,i}(j,3)-1,t)).^2)));
if(((60*srate/(Sub_sampl))*VV)>8 || ((60*srate/(Sub_sampl))*VV)<0.5)%speed of propagation between 0.5 to 8mm/min -> CSD candidate
SCR{t,i}(j)=0;
Vel_SCR{t,i}(j)=-1;
continue;
end
Vel_SCR{t,i}(j) = Vel_SCR{t,i}(j) + ((60*srate/(Sub_sampl))*VV);
q_tot = [-L_wind:L_wind];
f_SCR = zeros(size(q_tot,2),1);
for q_ind=1:size(q_tot,2)
q = q_tot(q_ind);
ii = i;
if (isempty(BB{t+q,ii})==0)
for w=1:size(BB{t+q,ii},1)
%avg speed of OBBox:
VV = mean(mean(sqrt(VX(BB{t+q,ii}(w,2):BB{t+q,ii}(w,2)+BB{t+q,ii}(w,4)-1,BB{t+q,ii}(w,1):BB{t+q,ii}(w,1)+BB{t+q,ii}(w,3)-1,t)).^2+ ...
(VY(BB{t+q,ii}(w,2):BB{t+q,ii}(w,2)+BB{t+q,ii}(w,4)-1,BB{t+q,ii}(w,1):BB{t+q,ii}(w,1)+BB{t+q,ii}(w,3)-1,t)).^2));
SCR_BB = mean(mean(SCR_tot(BB{t+q,ii}(w,2):BB{t+q,ii}(w,2)+BB{t+q,ii}(w,4)-1,BB{t+q,ii}(w,1):BB{t+q,ii}(w,1)+BB{t+q,ii}(w,3)-1,t)));
%Consistency check in speed and orientation:
Theta_degree = linspace(90,-10,size(II,1));
if(abs(BB{t+q,ii}(w,6)-BB{t,i}(j,6))<2)
Delta_BB = (abs(BB{t+q,ii}(w,6)-BB{t,i}(j,6))*75*Phi_scale*(pi/180)*cos((pi/180)*Theta_degree(min(BB{t+q,ii}(w,7),BB{t,i}(j,7)))))^2+...
(75*Theta_scale*(pi/180)*abs(BB{t+q,ii}(w,7)-BB{t,i}(j,7)))^2;
else
Delta_BB = (75*Phi_scale*(pi/180)*sum(cos((pi/180)*Theta_degree(round(linspace(min(BB{t+q,ii}(w,7),BB{t,i}(j,7)),max(BB{t+q,ii}(w,7),BB{t,i}(j,7)),abs(BB{t+q,ii}(w,6)-BB{t,i}(j,6)))))),2))^2+...
(75*Theta_scale*(pi/180)*abs(BB{t+q,ii}(w,7)-BB{t,i}(j,7)))^2;
end
if(BB{t+q,ii}(w,5)==BB{t,i}(j,5) && ...
(Delta_BB<(ceil(70*Sub_sampl/(srate*60))^2)) && ...
(Delta_BB>(ceil(0*Sub_sampl/(srate*60))^2)) && ...
((60*srate/(Sub_sampl))*VV)<=8 && (((60*srate/(Sub_sampl))*VV)>=0.5))% && ((LR_sgn*BB{t+q,ii}(w,1))<(LR_sgn*181)))
%OBBox score:
SCR{t,i}(j)=SCR{t,i}(j)+1;%SCR_BB;%(BB{t+q,ii}(w,4)*BB{t+q,ii}(w,3));
Vel_SCR{t,i}(j) = Vel_SCR{t,i}(j) + ((60*srate/(Sub_sampl))*VV);
%frame score (number of frames contributing
%in SCR):
f_SCR(q_ind) = 1;
end
end
end
end
if (size(find(f_SCR),1)<Threshld_Frame*size(f_SCR,1))% check if the presence of matching BBoxes is consistent over 2*L_wind frames
SCR{t,i}(j) = 0;
Vel_SCR{t,i}(j) = -1;
else
Vel_SCR{t,i}(j) = Vel_SCR{t,i}(j)/(SCR{t,i}(j)+1);
end
end
end
end
%Some adjustment at boundaries:
for t=size(SCR,1)-L_wind+1:size(SCR,1)
for i=1:size(SCR,2)
SCR{t,i} = [];
Vel_SCR{t,i} = [];
end
end
for t=1:L_wind
for i=1:size(SCR,2)
SCR{t,i} = [];
Vel_SCR{t,i} = [];
end
end
Threshld = 0.01;%:0.05:0.99
BSCR = SCR; %binary score of OBBox
CNT = zeros(size(SCR,1),1);%total score of frames
CNT_Vel = zeros(size(SCR,1),1);%total score of frames
for t=1:size(SCR,1)
temp=[];
for ii=1:size(SCR,2)
if (isempty(SCR{t,ii}))
continue;
end
temp=cat(1,temp,max(SCR{t,ii},[],1));
end
if (isempty(temp))
continue;
end
SCMax=max(temp);
for i=1:size(SCR,2)
if (isempty(SCR{t,i}))
continue;
end
for j=1:size(SCR{t,i},1)
if(SCR{t,i}(j)<Threshld*SCMax)% reject OBBoxes with very small score
BSCR{t,i}(j)=0;
else
BSCR{t,i}(j)=1;
CNT(t) = CNT(t)+SCR{t,i}(j);
CNT_Vel(t) = CNT_Vel(t)+(SCR{t,i}(j)*Vel_SCR{t,i}(j));
end
end
end
end
CNT_Vel(CNT~=0) = CNT_Vel(CNT~=0)./CNT(CNT~=0);
%% Temporal stitching:
% reject frames with very small total score
CNT(CNT<0.05*median(CNT)) = 0;
CNT_Vel(CNT==0) = -1;
CNT_T = bwconncomp(CNT);
CNT_TT = [];
for i=1:size(CNT_T.PixelIdxList,2)
CNT_TT = cat(1,CNT_TT,CNT_T.PixelIdxList{1,i});
end
BCNT = zeros(size(CNT)); % binary score of frames
WL = 1; % time window length threshold = 30 sec
%stitching time window = 2 min
WL_stitching = floor(2*60*srate/(Sub_sampl));
CNT_e = CNT;
for t=WL:size(CNT_e,1)-WL+1
temp = find(CNT_e(t-WL+1:t+WL-1));
if(size(temp,1)>=WL)
BCNT(t-WL+1) = 1;
end
end
% Reject small time windows & consider overlapping
% connected components (time samps) at boundaries:
BCNT_T = bwconncomp(BCNT);
for j=1:size(BCNT_T.PixelIdxList,2)
if(BCNT_T.PixelIdxList{1,j}(end,1)-BCNT_T.PixelIdxList{1,j}(1,1)+1<WL)
BCNT(BCNT_T.PixelIdxList{1,j}) = 0;
continue;
end
Indx = 0;
Strt = BCNT_T.PixelIdxList{1,j}(1);
for i=size(CNT_T.PixelIdxList,2):-1:1
while(size(find(CNT_T.PixelIdxList{1,i}(:,1)==(Strt-WL_stitching:Strt)))>0)
Indx = i;
Strt = CNT_T.PixelIdxList{1,Indx}(1,1)-1;
end
end
if(Indx~=0)
BCNT(CNT_T.PixelIdxList{1,Indx}(1,1):BCNT_T.PixelIdxList{1,j}(1)) = 1;
end
Indx = 0;
Endd = BCNT_T.PixelIdxList{1,j}(end);
for i=1:size(CNT_T.PixelIdxList,2)
while(size(find(CNT_T.PixelIdxList{1,i}(:,1)==(Endd:Endd+WL_stitching)))>0)
Indx = i;
Endd = CNT_T.PixelIdxList{1,Indx}(end,1)+1;
end
end
if(Indx~=0)
BCNT(BCNT_T.PixelIdxList{1,j}(end):CNT_T.PixelIdxList{1,Indx}(end,1)) = 1;
end
end
BCNT_T = bwconncomp(BCNT);
%% Find connected comps of BCNT
CNT_T = zeros(size(CNT));
CNT_T(CNT_TT) = 1;
BCNT_Vis = bwconncomp(BCNT);
Strt = [];
Endd = [];
Average_Vel = [];
for i=1:size(BCNT_Vis.PixelIdxList,2)
Vel_temp = CNT_Vel(BCNT_Vis.PixelIdxList{1,i}(1,1):BCNT_Vis.PixelIdxList{1,i}(end,1));
Average_Vel = cat(1,Average_Vel,mean(Vel_temp(Vel_temp>0)));
Strt = cat(1,Strt,uint32((T_start+Sub_sampl*BCNT_Vis.PixelIdxList{1,i}(1,1))/srate));
Endd = cat(1,Endd,uint32((T_start+Sub_sampl*BCNT_Vis.PixelIdxList{1,i}(end,1))/srate));
end
Thr_ind = Thr_ind+1;
Average_Vel_tot_temp_temp{Thr_ind} = Average_Vel;
Strt_tot_temp_temp{Thr_ind} = Strt;
Endd_tot_temp_temp{Thr_ind} = Endd;
%% Performance calculation:
% Find all detection predictions for given
% ID/Session/Part using parameters Thr1-4
CSD_GT = CSD_GT_temp;
Events = Events_temp;
CSD_GT(end) = [];
Events(end) = [];
EEG_mask_tot = EEG_mask(:,1:size(EEG_mask,2)-1);
num_good_elec_craniectomy = (sum(EEG_mask_tot(crani_ind,:),1))';
CC_BW = squeeze(sum(sum(value,1),2))';
num_good_elec = num_good_elec_craniectomy;
Trial_boundary_indicators = zeros(size(BCNT));
delta_trial = 30*(srate*1)/Sub_sampl;
L_trial = 2*(srate*60)/Sub_sampl;
Trial_boundary_indicators(delta_trial:delta_trial:(size(BCNT,1)-L_trial)) = 1;
Trial_boundary_indicators(1) = 1;
Trial_boundary_ind = find(Trial_boundary_indicators);
Trial_boundary_ind(1) = 0;
T_start_tot_sub = zeros(1,size(Trial_boundary_ind,1));
T_end_tot_sub = zeros(1,size(Trial_boundary_ind,1));
BCNT_tot_sub = zeros(L_trial,(size(Trial_boundary_ind,1)));
CNT_tot_sub = zeros(L_trial,(size(Trial_boundary_ind,1)));
CSD_GT_tot_sub = zeros(L_trial,(size(Trial_boundary_ind,1)));
num_good_elec_tot_sub = zeros(L_trial,(size(Trial_boundary_ind,1)));
CSD_type_tot_sub = cell(1,size(Trial_boundary_ind,1));
for i_sub = 1:size(Trial_boundary_ind,1)
Strt = Trial_boundary_ind(i_sub)+1;
Endd = (Trial_boundary_ind(i_sub)+L_trial);
T_start_sub = T_start+((i_sub-1)*delta_trial*Sub_sampl);
T_end_sub = (T_start_sub+(L_trial*Sub_sampl))+10*srate;
T_start_tot_sub(i_sub) = T_start_sub;
T_end_tot_sub(i_sub) = T_end_sub;
BCNT_tot_sub(:,i_sub) = BCNT(Strt:Endd);
CNT_tot_sub(:,i_sub) = CNT_Vel(Strt:Endd);
CSD_GT_tot_sub(:,i_sub) = CSD_GT(Strt:Endd);
CSD_type_counter = find(CSD_GT);
CSD_type_ind_strt = min(find(CSD_type_counter>=Strt));
CSD_type_ind_Endd = max(find(CSD_type_counter<=Endd));
if(~isempty(CSD_type_ind_strt) && ~isempty(CSD_type_ind_Endd))
if(CSD_type_ind_strt<=CSD_type_ind_Endd)
CSD_type_tot_sub(i_sub) = {CSD_type(CSD_type_ind_strt:CSD_type_ind_Endd)};
end
end
num_good_elec_tot_sub(:,i_sub) = num_good_elec(Strt:Endd);
end
% Save all threshold-combination results
T_start_tot_temp_temp{Thr_ind} = T_start_tot_sub;
T_end_tot_temp_temp{Thr_ind} = T_end_tot_sub;
BCNT_tot_temp_temp{Thr_ind} = reshape(BCNT_tot_sub,[],1);
CNT_tot_temp_temp{Thr_ind} = reshape(CNT_tot_sub,[],1);
CSD_GT_tot_temp_temp{Thr_ind} = reshape(CSD_GT_tot_sub,[],1);
num_good_elec_tot_temp_temp{Thr_ind} = reshape(num_good_elec_tot_sub,[],1);
CSD_type_tot_temp_temp{Thr_ind} = CSD_type_tot_sub;
end
end
end
end
% Save each threshold-combination specific result for each Part
Average_Vel_tot_temp{CSD_ind_orig} = Average_Vel_tot_temp_temp;
Strt_tot_temp{CSD_ind_orig} = Strt_tot_temp_temp;
Endd_tot_temp{CSD_ind_orig} = Endd_tot_temp_temp;
T_start_tot_temp{CSD_ind_orig} = T_start_tot_temp_temp;
T_end_tot_temp{CSD_ind_orig} = T_end_tot_temp_temp;
BCNT_tot_temp{CSD_ind_orig} = BCNT_tot_temp_temp;
CNT_tot_temp{CSD_ind_orig} = CNT_tot_temp_temp;
CSD_GT_tot_temp{CSD_ind_orig} = CSD_GT_tot_temp_temp;
num_good_elec_tot_temp{CSD_ind_orig} = num_good_elec_tot_temp_temp;
CSD_type_tot_tempp{CSD_ind_orig} = CSD_type_tot_temp_temp;
end
% End of Part-specific loop
currentFolder = pwd;
cd ..
%% Performance Metric Calculations
TPR_num_TOTAL = {};
TPR_denum_TOTAL = {};
TNR_num_TOTAL = {};
TNR_denum_TOTAL = {};
CSD_event_quality_tot_TOTAL = {};
Thr_ind = 0;
CSD_GT_total_temp = CSD_GT_total{1};
CSD_Labels_total_temp = CSD_Labels_total{1};
% Loop back through thres-combinations for TPR/FNR calculations at each
% Session of given Patient-ID
for Thr_3_ind = 1:size(THR_3,2)
for Thr_1_ind = 1:size(THR_1,2)
for Thr_2_ind = 1:size(THR_2,2)
TPR_num_TOTAL_temp = [];
TPR_denum_TOTAL_temp = [];
TNR_num_TOTAL_temp = [];
TNR_denum_TOTAL_temp = [];
CSD_event_quality_tot_TOTAL_temp = [];
Endd_tot_TOTAL = {};
Strt_tot_TOTAL = {};
Thr_1 = THR_1(Thr_1_ind)
Thr_2 = THR_2(Thr_2_ind)
Thr_3 = THR_3(Thr_3_ind)
for Thr_4_ind = 1:size(THR_4,2)
Endd_tot_TOTAL_temp = [];
Strt_tot_TOTAL_temp = [];
Average_Vel_tot_TOTAL_temp = [];
Thr_4 = THR_4(Thr_4_ind)
CSD_GT_total = CSD_GT_total_temp;
CSD_Labels_total = CSD_Labels_total_temp;
BCNT_tot = {};
CNT_tot = {};
CSD_GT_tot = {};
T_start_tot = {};
T_end_tot = {};
CSD_type_tot = {};
num_good_elec_tot = {};
Strt_tot = {};
Endd_tot = {};
Average_Vel_tot = {};
Thr_ind = Thr_ind+1;
for CSD_ind_orig = 1:Part_size
T_start_tot{CSD_ind_orig} = T_start_tot_temp{CSD_ind_orig}{Thr_ind};
T_end_tot{CSD_ind_orig} = T_end_tot_temp{CSD_ind_orig}{Thr_ind};
BCNT_tot{CSD_ind_orig} = BCNT_tot_temp{CSD_ind_orig}{Thr_ind};
CNT_tot{CSD_ind_orig} = CNT_tot_temp{CSD_ind_orig}{Thr_ind};
CSD_GT_tot{CSD_ind_orig} = CSD_GT_tot_temp{CSD_ind_orig}{Thr_ind};
num_good_elec_tot{CSD_ind_orig} = num_good_elec_tot_temp{CSD_ind_orig}{Thr_ind};
CSD_type_tot{CSD_ind_orig} = CSD_type_tot_tempp{CSD_ind_orig}{Thr_ind};
Average_Vel_tot{CSD_ind_orig} = Average_Vel_tot_temp{CSD_ind_orig}{Thr_ind};
Strt_tot{CSD_ind_orig} = Strt_tot_temp{CSD_ind_orig}{Thr_ind};
Endd_tot{CSD_ind_orig} = Endd_tot_temp{CSD_ind_orig}{Thr_ind};
end
TPR_num = 0;
TPR_denum = 0;
TNR_num = 0;
TNR_denum = 0;
Tot_detectable_CSD = 0;
Detected_CSD = 0;
CSD_event_quality_tot = [];
CSD_type_tot_temp = [];
for type_i = 1:size(CSD_type_tot,2)
tmp = CSD_type_tot{1,type_i};
for type_j = 1:size(tmp,2)
tmp_temp = tmp{1,type_j};
for type_l = 1:size(tmp_temp,1)
CSD_type_tot_temp = cat(2,CSD_type_tot_temp,tmp_temp(type_l));
end
end
end
CSD_type_tot = CSD_type_tot_temp;
T_start_TOT = [];
T_end_TOT = [];
BCNT_TOT = [];
CNT_TOT = [];
CSD_GT_TOT = [];
num_good_elec_TOT = [];
for ind_CSD = 1:Part_size
T_start_TOT = cat(2,T_start_TOT,T_start_tot{ind_CSD});
T_end_TOT = cat(2,T_end_TOT,T_end_tot{ind_CSD});
BCNT_TOT = cat(1,BCNT_TOT,BCNT_tot{ind_CSD});
CNT_TOT = cat(1,CNT_TOT,CNT_tot{ind_CSD});
CSD_GT_TOT = cat(1,CSD_GT_TOT,CSD_GT_tot{ind_CSD});
num_good_elec_TOT = cat(1,num_good_elec_TOT,num_good_elec_tot{ind_CSD});
end
T_start_tot = T_start_TOT;
T_end_tot = T_end_TOT;
BCNT_tot = BCNT_TOT;
CNT_tot = CNT_TOT;
CSD_GT_tot = CSD_GT_TOT;
num_good_elec_tot = num_good_elec_TOT;
srate = 64;
Sub_sampl = 30*srate;
%% Calculation of correct alarm and detection rate:
Trial_boundary_indicators = zeros(size(BCNT_tot));
delta_trial = size(Trial_boundary_indicators,1)/size(T_start_tot,2);
Trial_boundary_indicators(delta_trial:delta_trial:end) = 1;
Trial_boundary_indicators(1) = 1;
Trial_boundary_ind = find(Trial_boundary_indicators);
Trial_boundary_ind(1) = 0;
CSD_GT_tot_ind = find(CSD_GT_tot);
NO_CSD_events_ind = (contains(CSD_type_tot,'No CSD'));
CSD_GT_tot(CSD_GT_tot_ind(NO_CSD_events_ind)) = 0;
CSD_type_tot(NO_CSD_events_ind) = [];
ISD_events_ind = (contains(CSD_type_tot,'_ISD'));
CSD_GT_tot_ind = find(CSD_GT_tot);
CSD_GT_tot_with_ISD = CSD_GT_tot;
CSD_GT_tot(CSD_GT_tot_ind(ISD_events_ind)) = 0;
CSD_type_tot(ISD_events_ind) = [];
num_good_elec_tot_Avg = num_good_elec_tot;
num_good_elec_tot(CSD_GT_tot_ind(ISD_events_ind)) = 0;
CSD_type_tot_unique = unique(CSD_type_tot);