--- a
+++ b/featurebased-approach/TrainClassifier.m
@@ -0,0 +1,168 @@
+function TrainClassifier(feature_file)
+% This function extracts features for each record present  in a folder
+%
+%  Input:
+%       - feature_file:         file containing table with extracted
+%                               features in different records
+%       
+% --
+% ECG classification from single-lead segments using Deep Convolutional Neural 
+% Networks and Feature-Based Approaches - December 2017
+% 
+% Released under the GNU General Public License
+%
+% Copyright (C) 2017  Fernando Andreotti, Oliver Carr
+% University of Oxford, Insitute of Biomedical Engineering, CIBIM Lab - Oxford 2017
+% fernando.andreotti@eng.ox.ac.uk
+%
+% 
+% For more information visit: https://github.com/fernandoandreotti/cinc-challenge2017
+% 
+% Referencing this work
+%
+% Andreotti, F., Carr, O., Pimentel, M.A.F., Mahdi, A., & De Vos, M. (2017). 
+% Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect 
+% Arrhythmia from Short Segments of ECG. In Computing in Cardiology. Rennes (France).
+%
+% Last updated : December 2017
+% 
+% This program is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+% 
+% This program is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+% 
+% You should have received a copy of the GNU General Public License
+% along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+load(feature_file)
+NFEAT=size(allfeats,2);
+NFEAT=NFEAT-2;
+
+% Get summary statistics on the distribution of the features in each
+% signal, using following:
+% - median
+% - inter quartile range
+% - range
+% - min value
+% - max value
+% - 25% perctile
+% - 50% perctile
+% - 75% percentile
+% - Real coefficients of Hilbert transform 
+% - Absolute values of Hilbert transform
+% - Skewness
+% - Kurtosis
+% 
+feat = zeros(max(allfeats.rec_number),16*NFEAT);
+for i=1:max(allfeats.rec_number)
+    fprintf('Processing record %d .. \n',i)
+    ind=find(table2array(allfeats(:,1))==i);
+    feat(i,1:NFEAT)=nanmean(table2array(allfeats(ind,3:end)));
+    feat(i,1*NFEAT+1:2*NFEAT)=nanstd(table2array(allfeats(ind,3:end)));
+    if length(ind)>2
+        PCAn=pca(table2array(allfeats(ind,3:end)));
+        feat(i,2*NFEAT+1:3*NFEAT)=PCAn(:,1);
+        feat(i,3*NFEAT+1:4*NFEAT)=PCAn(:,2);
+    else
+        feat(i,2*NFEAT+1:3*NFEAT)=NaN;
+        feat(i,3*NFEAT+1:4*NFEAT)=NaN;
+    end
+    feat(i,4*NFEAT+1:5*NFEAT)=nanmedian(table2array(allfeats(ind,3:end)));
+    feat(i,5*NFEAT+1:6*NFEAT)=iqr(table2array(allfeats(ind,3:end)));
+    feat(i,6*NFEAT+1:7*NFEAT)=range(table2array(allfeats(ind,3:end)));
+    feat(i,7*NFEAT+1:8*NFEAT)=min(table2array(allfeats(ind,3:end)));
+    feat(i,8*NFEAT+1:9*NFEAT)=max(table2array(allfeats(ind,3:end)));
+    feat(i,9*NFEAT+1:10*NFEAT)=prctile(table2array(allfeats(ind,3:end)),25);
+    feat(i,10*NFEAT+1:11*NFEAT)=prctile(table2array(allfeats(ind,3:end)),50);
+    feat(i,11*NFEAT+1:12*NFEAT)=prctile(table2array(allfeats(ind,3:end)),75);
+    HIL=hilbert(table2array(allfeats(ind,3:end)));
+    feat(i,12*NFEAT+1:13*NFEAT)=real(HIL(1,:));
+    feat(i,13*NFEAT+1:14*NFEAT)=abs(HIL(1,:));
+    feat(i,14*NFEAT+1:15*NFEAT)=skewness(table2array(allfeats(ind,3:end)));
+    feat(i,15*NFEAT+1:16*NFEAT)=kurtosis(table2array(allfeats(ind,3:end))); 
+end
+
+In = feat;
+Ntrain = size(In,1);
+In(isnan(In)) = 0;
+% Standardizing input
+In = In - mean(In);
+In = In./std(In);
+
+labels = {'A' 'N' 'O' '~'};
+Out = reference_tab{:,2};
+Outbi = cell2mat(cellfun(@(x) strcmp(x,labels),Out,'UniformOutput',0));
+Outde = bi2de(Outbi);
+Outde(Outde == 4) = 3;
+Outde(Outde == 8) = 4;
+clear Out
+rng(1); % For reproducibility
+%% Perform cross-validation
+%== Subset sampling
+k = 5;
+cv = cvpartition(Outde,'kfold',k);
+confusion = zeros(4,4,k);
+F1save = zeros(k,4);
+F1_best = 0;
+for i=1:k
+    fprintf('Cross-validation loop %d \n',i)
+    trainidx = find(training(cv,i));
+    trainidx = trainidx(randperm(length(trainidx)));
+    testidx  = find(test(cv,i));
+    %% Bagged trees (oversampled)
+    ens = fitensemble(In(trainidx,:),Outde(trainidx),'Bag',50,'Tree','type','classification');
+    [~,probTree] = predict(ens,In(testidx,:));
+    
+    %% Neural networks
+    net = patternnet(10);
+    net = train(net,In(trainidx,:)',Outbi(trainidx,:)');            
+    probNN = net(In(testidx,:)')';    
+    
+    %% Combining methods
+    C = cat(3,probTree,probNN);
+    C = mean(C,3);
+    estimate = zeros(size(C,1),1);
+    for r = 1:size(C,1)
+        [~,estimate(r)] = max(C(r,:));
+    end
+    confmat = confusionmat(Outde(testidx),estimate);
+    confusion(:,:,i) = confmat;    
+    F1 = zeros(1,4);
+    for j = 1:4
+        F1(j)=2*confmat(j,j)/(sum(confmat(j,:))+sum(confmat(:,j)));
+        fprintf('F1 measure for %s rhythm: %1.4f \n',labels{j},F1(j))
+    end
+    F1save(i,:) = F1;
+    
+    if F1 > F1_best
+        F1_best = F1;
+        ensTree_best = compact(ens);
+        nnet_best = net;
+    end
+end
+%% Producing statistics
+confusion = sum(confusion,3);
+F1 = zeros(1,4);
+for i = 1:4
+    F1(i)=2*confusion(i,i)/(sum(confusion(i,:))+sum(confusion(:,i)));
+    fprintf('F1 measure for %s rhythm: %1.4f \n',labels{i},F1(i))
+end
+fprintf('Final F1 measure:  %1.4f\n',mean(F1))
+
+%% Save output
+save('results_allfeat.mat','F1save','F1_best')
+save('ensTree.mat','ensTree_best')
+save('nNets.mat','nnet_best')
+
+
+
+
+
+
+
+