%AFF_fill.m
% Fill gaps by Averaged Feedback of a Feedforward neural network model
% In response to 2010 physionet challenge -- Mind the Gap, the software
% fills signal gaps. The program is based on a feedforward neural network
% model. We adopt the following techniques to improve the prediction
% accuracy.
% 1. Parameters of the network are optimized for better performance.
% 2. Mix of short- and long-term memory.
% 3. Iteration of feedback on the results of the feedforward model.
% 4. Average of the results from the feedback iterations.
% The code has been tested on Matlab 2008a and Matlab 2010a.
%
% Copyright (C) 2010
% Xiaopeng Zhao <xzhao9@utk.edu>
% Nonlinear Biodynamics Laboratory
% Department of Biomedical Engineering
% University of Tennessee
% Knoxville, TN 37996
%
% This software is released under the terms of the GNU General
% Public License (http://www.gnu.org/copyleft/gpl.html).
clear all
patientclass='c';
filepath=['../physionetdata/set-' patientclass '/'];
numofiteration=3;
datapoints=10*3750;
myepochs=40;
mydelay=[0:5,10,20,50,100,200];
mygoal=0;
myperfFcn='msne';
mylayers=[10,1];
mytransferfcn={'tansig' 'purelin' };
yp{numofiteration}=[];
yrec{numofiteration}=[];
for pid=0:99
if pid < 10
patientid = [ '0' num2str(pid) ];
else
patientid = num2str(pid);
end
filenm=[patientclass patientid 'm.mat'];
load([filepath,filenm]);
disp(['---------------------------------I am working on ', filenm]);
%identify input channels and target channel
totalSignalNum = size(val, 1 );
missing_channel=[];
input_channels=[];
for i = 1 : totalSignalNum
if val(i, end-3750+1:end) == 0
missing_channel=[missing_channel,i];
else
input_channels=[input_channels,i];
end
end
%if multiple channels have missing data, we check if the channel is
%really a missing channel.
miss_id=1;
for i=1:length(missing_channel)
if val(missing_channel(i),1:1000)==0
else
miss_id=i;
end
end
missing_channel=missing_channel(miss_id);
%input and output data
inpdata=val(input_channels,:); %
outdata=val(missing_channel,:); %
inpdata=removeconstantrows(inpdata);
[inpdata, inps] = mapminmax(inpdata);
[outdata, outs] = mapminmax(outdata);
%augment the input data to consider delays
[inputnum,totlen] = size(inpdata);
dmax=max(mydelay); dnum=length(mydelay);
x=zeros(dnum*inputnum,totlen-dmax);
for i=1:length(mydelay)
di=mydelay(i);
pos=(i-1)*inputnum+(1:inputnum);
x(pos,:)=inpdata(:,dmax+1-di:totlen-di);
end
y=outdata(dmax+1:end);
%feebforward prediction
indx=size(x,2)-datapoints+1-3750:size(x,2)-3750;
p = x(:,indx);
t = y(:,indx);
clear mynet
mynet = newff(p,t, mylayers, mytransferfcn );
mynet.performFcn=myperfFcn;
mynet.trainParam.show = 5;
mynet.trainParam.showWindow = false;
mynet.trainParam.epochs = myepochs;
mynet.trainParam.goal=mygoal;
[mynet,tr] = train(mynet,p,t);
xp = x(:,end-3750+1:end);
yp{1} = sim( mynet, xp );
yrec{1}=mapminmax('reverse',yp{1}, outs)';
%feedback
indx=size(x,2)-datapoints+1:size(x,2);
p = x(:,indx);
for i=2:numofiteration
y(end-3750+1:end)=yp{i-1};
t = y(:,indx);
clear mynet
mynet = newff(p,t, mylayers, mytransferfcn );
mynet.performFcn=myperfFcn;
mynet.trainParam.epochs = myepochs;
mynet.trainParam.goal=mygoal;
mynet.trainParam.show = 5;
mynet.trainParam.showWindow = false;
[mynet,tr] = train(mynet,p,t);
yp{i} = sim(mynet, xp);
yrec{i}=mapminmax('reverse',yp{i}, outs)';
end
ydata=cell2mat(yrec);
yavg=mean(ydata,2);
save([filenm(1:3),'.predicted'],'yavg','-ascii');
end