how to download the dataset

[lyzhangjm]

Hi, how to download the dataset ? the website http://www.physionet.org/pn3/shhpsgdb/ can't download

[Sebelino]

Hello, you can download the dataset I used (SHHS1) from the NSRR website. The data isn't exactly freely accessible; you will have to fill out a form to access it. Scroll down to the bottom of the page and click "Fill out a Data Access and Use Agreement".

[lyzhangjm]

Thanks for your help. it is difficult to access the SHHS1. Can other dataset(edf format) from http://www.physionet.org be used ?

[Sebelino]

It should work with other EDF files, but you may have to make some adjustments to lines 482-492 and 535-560 in score.m before it works. Which EDF dataset were you planning to use?

Alternatively, you could test the program with the slp01a record which you can download without any hassle. I wrote some instructions on how to use wfdb2mat to generate mat- and hea-files from it which the program is going to need to work. Please feel free to ask if you are wondering about the details; I haven't put much effort into making the software user-friendly yet.

[Sebelino]

BTW I just added a little tutorial to the readme; I hope it helps.

[lyzhangjm]

the edfread.m is not right, it is html format, rather than matlab code

[lyzhangjm]

I use matlab 2015b, but this position（ [numerator,denominator] = str2fraction(tokens{2}); ） have error
Undefined function or variable 'tokens'.
Error in score>str2fraction (line 450)
numerator = str2num(tokens{2});
I have change the function str2fraction(tokens{2}) into str2fraction(tokens{2},tokens), then the error disappearing. However, other error is appearing .

I run the code as follows:
vectors = score('load shhs1-200001 | segment 3 | extract | select Mean Variance');
score(vectors,'bundle 12RW 34M | partition 0.25 | svm | eval | plot');
the error information is :
312 stream.svm = SVM(stream.trainingset,tokens{2});
Index exceeds matrix dimensions.
Error in score (line 312)
stream.svm = SVM(stream.trainingset,tokens{2});

[Sebelino]

Regarding edfread.m, you'd need to follow the link and then copy the code off the webpage.

str2fractions(tokens{2}) should be str2fraction(fracstr); it appears that Matlab 2015b added some static program analysis which catches errors that 2014b would not. I updated the file now anyway.

As for the latter error, try svm linear or svm rbf instead of just svm. I added support for specifying the kernel at some point, but forgot to update the documentation. Also you may want to do something like bundle 12RW 3 instead since it does not contain any "M" or "4" labels (slp01a does, however).

[Sebelino]

There turned out to be a few bugs in the eval and plot filters as well but they should work now. The following works as expected:

>> score('load 200001 | segment 1 | extract | select Mean Variance | bundle W 123R | partition 3:1 | svm linear | eval | plot')
Reading cache/shhs.shhs1-200001.mat...
ans = 
        trainingset: [813x1 LabeledFeaturevector]
         testingset: [271x1 LabeledFeaturevector]
                svm: [1x1 SVM]
       predictedset: [271x1 LabeledFeaturevector]
           accuracy: 0.8413
    confusionmatrix: [2x2 double]
     confusionorder: [2x1 char]

[lyzhangjm]

I use matlab 2014b, the error as follows:

score('load 200001 | segment 1 | extract | select Mean Variance | bundle W 123R | partition 3:1 | svm linear | eval | plot')
Reading cache/shhs.shhs1-200001.mat...
10 predictormatrix = labeledfeaturevectors.matrix();
Error using horzcat
Dimensions of matrices being concatenated are not consistent.
Error in score (line 320)
m = [tlabels,plabels];

is it m = [tlabels;plabels] ? the Dimensions of tlabels are different with that of plabels

[Sebelino]

Did you try the most recent version? I uploaded a fix for that bug 30 minutes ago.

[lyzhangjm]

the following are the debug information:

score('load 200001 | segment 1 | extract | select Mean Variance | bundle W 123R | partition 3:1 | svm rbf | eval | plot')
Reading cache/shhs.shhs1-200001.mat...
320 m = [tlabels,plabels];
K>> size(tlabels)
ans =
271 1
K>> size(plabels)
ans =
1 1

the Dimensions of tlabels are different with that of plabels. hence, this position is wrong :
m = [tlabels,plabels];

[lyzhangjm]

yes, I download the recent version, but the error appear either

[Sebelino]

That's odd... Just to make sure, is this the contents of the predict(...) function in your SVM.m?:

        function featureset = predict(self,predictors)
            % Predicts the labels for the given data
            alllabels = [];
            for f = fieldnames(self.Model)'
                [labels,score] = self.Model.(f{:}).predict(predictors.matrix());
                alllabels = [alllabels,labels];
            end
            %[M,F,C] = mode(uint8(alllabels)'); %TODO
            winnerlabels = mode(alllabels,2);
            featureset = arrayfun(@(i){LabeledFeaturevector(predictors(i).Vector,winnerlabels(i))},(1:size(winnerlabels,1)));
            featureset = [featureset{:}]';
        end

It returns a 271x1 LabeledFeaturevector for me. It used to return a single LabeledFeaturevector until I fixed it, hence the bug.

[lyzhangjm]

function featureset = predict(self,predictors)
% Predicts the labels for the given data
alllabels = [];
for f = fieldnames(self.Model)'
[labels,score] = predict(self.Model.(f{:}),predictors.matrix());
alllabels = [alllabels,labels];
end
%[M,F,C] = mode(uint8(alllabels)'); %TODO
winnerlabels = mode(alllabels')';
featureset = arrayfun(@(i){LabeledFeaturevector(predictors(i).Vector,winnerlabels(i))},(1:size(winnerlabels,1)));
featureset = [featureset{:}]';
end

[lyzhangjm]

the size of alllabels is right(271), but the size of winnerlabels is 1