function feats = ecgsqi(signal,qrs,fs)

% This function calculates multiple SQIs for electrocardiogram (ECG) signals.

%

% Input:

%     signal            ECG signal (function only supports vectors)

%     fs                sampling frequency (in Hz)

%

% Output:

%     sqi               Resulting SQI for ABP signal

%    qrs_out            QRS samplestamps

%

%

% --

% 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/>.

%% Parameters

WIN_ACCEPT = 0.10;    % acceptance interval for FP in QRS detection (in s)

WIN_QRS = 0.1;         % window used to delimit QRS complexes (in s)

% Temporal SQIs

% feat_flat = flatsqi(signal);  % flatline detection

feat_stdsqi  = stdsqi(signal);   % standard deviation

feat_ksqi  = ksqi(signal);   % standard deviation

feat_ssqi  = ssqi(signal);   % standard deviation

% Frequency bands

feat_psqi = psqi(signal,fs,[15 45],[0 100]);

% Detection-based SQIs

combs = nchoosek(1:length(qrs),2);

combs = num2cell(combs,2);

feat_bsqi = cellfun(@(x) bsqi(qrs{x(1)},qrs{x(2)},WIN_ACCEPT,fs),combs);

feat_rsqi = arrayfun(@(x) rsqi(qrs{x},fs,0.96),1:length(qrs));

feat_csqi = arrayfun(@(x) csqi(signal,qrs{x},fs,WIN_QRS),1:length(qrs));

feat_xsqi = arrayfun(@(x) xsqi(signal,qrs{x},fs,WIN_QRS),1:length(qrs));

feats = [feat_stdsqi, feat_ksqi, feat_ssqi, ...

    feat_psqi, feat_bsqi', feat_rsqi, feat_csqi, feat_xsqi];

feats(isnan(feats)) = 0; % making sure there are no NaNs