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





fernandoandreotti
github-repos/MATLAB/fernandoandreotti-cinc-challenge2017/cinc-challenge2017-78cfc8e6194857cee0cd731f41ba5b2dd589aed2/featurebased-approach/subfunctions/ecgsqi.m
cinc-challenge2017
github-repos/MATLAB/fernandoandreotti-cinc-challenge2017
78cfc8e6194857cee0cd731f41ba5b2dd589aed2