%% =============== Demo ======================== %%

%% ============== Licensce ========================================== %%

% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 

% "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 

% LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 

% FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT

% OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 

% SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED

% TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 

% PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 

% LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 

% NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 

% SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

% Author :

% Hooman Sedghamiz, Feb, 2018

% MSc. Biomedical Engineering, Linkoping University

% Email : Hooman.sedghamiz@gmail.com

%--------------- Only Hit the Run button -----------------%

%% ===================== DEMO FOR QRS DETECTORS ======================== %%

%---------------- Load a Sample ECG Signal from (\SampleSignals) directory -----------------%

slashchar = char('/'*isunix + '\'*(~isunix)); 

load([fileparts(which(mfilename)),slashchar,'SampleSignals',slashchar,...

    'ECG1.mat']);

addpath(genpath(fileparts(which(mfilename))));

load('ECG1.mat');

% ------------------ Call the BioSigKit Wrapper -------------------%

Analysis = RunBioSigKit(EKG1,250,0);          % Uses ECG1 as input,Fs=250

%-------------------- Call Pan Tompkins Algorithm ------------------- %

Analysis.MTEO_qrstAlg;                        % Runs MTEO algorithm

QRS = Analysis.Results.R;                     % Stores R peaks in QRS

%-------------------- Call MTEO QRS ------------------- %

Analysis.MTEO_qrstAlg;                        % Runs MTEO algorithm

QRS = Analysis.Results.R;                     % Stores R peaks in QRS

Qwave = Analysis.Results.Q;                   % Indice of Q peaks

Swave = Analysis.Results.S;                   % Indice of S peaks

Twave = Analysis.Results.T;                   % Indice of T peaks

Pwave = Analysis.Results.P;                   % Indice of P peaks

% ------------------------ Change Sample rate ------------------%

Analysis.Fs =360;

%--------------------------- Open GUI -----------------------------%

% Analysis = RunBioSigKit();                    % Opens GUI 

%% ==================  DEMO FOR OTHER SUBROUTINES =================== %%

% ----------------- Activity detection with hilber Transform -------------%

Analysis.PlotResult = 1;                           % Set the plot flag on

Analysis.Env_hilbert;                              % detect activities

v = repmat([.1*ones(200,1);ones(100,1)],[10 1]);   % generate true variance profile

Analysis.Sig = sqrt(v).*randn(size(v));            % Add white noise

Analysis.Env_hilbert;

Analysis.Sig = EKG1;

% ---------------- Compute mobility and complexity ------------------- %

[mobility,complexity] = Analysis.ComputeHjorthP;

fprintf(['Mobility = ',mat2str(mobility),'\n']);

fprintf(['Complexity = ',mat2str(complexity),'\n']);

%--------------------- Template matching -------------------------------%

template = Analysis.Sig(QRS(2)-60:QRS(2)+100);          % extract a heart-beat

[PsC_s,best_lag] = Analysis.TemplateMatch(template);    % Find it in the signal

fprintf(['PsuedoScore = ',mat2str(PsC_s),'\n']);

fprintf(['Location = ',mat2str(best_lag),'\n']);

figure,plot(Analysis.Sig);

hold on,plot((best_lag:best_lag+160),...

    Analysis.Sig(best_lag:best_lag+160));               % Highlight the template in Sig

title('PsuedoCorrelation Demo...');

% ------- Fetal ECG extraction in real time with neural PCA -------------%

load([fileparts(which(mfilename)),slashchar,'SampleSignals',slashchar,...

    'foetal_ecg.dat']);                                  % Load Foeatal ECG

Analysis.Sig = foetal_ecg(:,2:9)';                       % Load all the 8 channels

[~,PC] = Analysis.neural_pca(8,2);                       % get the PCs

figure;

for i = 1: 5

    subplot(5,1,i),plot(PC(i,:));

end

title('Foetal Beat detection with real-time neural-PCA...');

% ------------------------- Adaptive Filters ---------------------------%

Analysis.Sig = EKG1;

Analysis.Fs=250;

out = Analysis.adaptive_filter(2,[],250);                % Use adaptive line enhancer (type =2)

figure,plot(Analysis.Sig/max(Analysis.Sig));

hold on,plot(out/max(out));

title('Adaptive Line Enhancer with 1 Sec Delay');

% ------------------------- ECG derived Respiration --------------------%

Analysis.PlotResult = 0;                           % Set the plot flag of0

EDR = Analysis.EDR_comp;

figure,plot(Analysis.Sig/max(Analysis.Sig));

hold on,plot(EDR/max(EDR));

title('ECG Derived Resp Signal');

% ------- Single Channel Foetal ECG extraction with nonlinear filter -------------%

Analysis.Sig = foetal_ecg(:,2)';                                  % Load channel 2

Analysis.Fs=250;

output = Analysis.nonlinear_phase_filt(1, 50, 45, 1500);          % run the filter

figure,plot(Analysis.Sig);

hold on,plot(Analysis.Sig - output);

title('Foetal ECG extracted...');

% ----------------- ACC derived respiration ----------------------% 

load([fileparts(which(mfilename)),slashchar,'SampleSignals',slashchar,...

    'ACC.mat']);

Analysis.Sig = ACC';

Analysis.Fs = 200;

EDR = Analysis.ADR_comp;

figure,plot(Analysis.Sig/max(Analysis.Sig));

hold on,plot(EDR/max(EDR));

title('ECG Derived Resp Signal');

%---------------- ACC processing and Posture detection ---------------%

[output,state,EE,F,SMA] = Analysis.ACC_Act;

States ={'Steady','Slight Movement','High Activity'};

fprintf(['State is ',States{state+1},' and Energy Expenditure = ',mat2str(EE),'\n']);