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updated Background color
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@hooman650
hooman650 committed Oct 14, 2018
1 parent 33be6ae commit 987b1d5
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568 changes: 0 additions & 568 deletions @BioSigKit/BioSigKit.asv
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2 changes: 1 addition & 1 deletion @BioSigKit/BioSigKit.m
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Expand Up @@ -345,7 +345,7 @@ function visualizeResults(obj)
if ~isempty(obj.Results)
PP = fields(obj.Results);
Index = [5,10,15;3,8,13;2,7,12;1,6,11;4,9,14];
colors = distinguishable_colors(length(PP));
colors = distinguishable_colors(length(PP),{'k','y'});
for i= 1: length(PP)
% ----------- Update Signal ------------------ %
line(repmat(obj.Results.(PP{i})(1,:),[2 1]),...
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261 changes: 261 additions & 0 deletions Algorithms/WaveletTransformEventDetection.m
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function [PeakPositions] = ...
WaveletTransformEventDetection(signal, fs, wave )
%WAVELETTRANSFORMEVENTDETECTION identifies one wave event in a given search
%window. This is an adapted version of the algorithm described in the
%following publication:
% Martinez, J. P. et al. (2004). A Wavelet-Based ECG Delineator:
% Evaluation on Standard Databases. IEEE Transactions on Biomedical
% Engineering, 51(4):570-581.
%
% Inputs:
% signal - 1-dimensional signal;
% data / signal values
% fs - sampling rate of signal
% searchWindow - struct with searchWindow.start, searchWindow.end
% searchWindow.rPeaks
% searchWindow.start and searchWindow.end in samples
% definee the search region within the signal
% searchWindow.rPeaks gives the start of the
% considered RR interval (1st column) and the end of
% the considered RR interval (2nd column)
% wave - considered wave; either 'P' for P-wave detection or 'T'
% for T-wave detection (thresholds are correspondingly adapted)
%
% Output:
% PeakPositions - struct PeakPositions.MaxPeak with identified
% peak in the range of the searchWindow
%
% BY Heike Leutheuser, 31.03.2016, heike.leutheuser@fau.de
%
% Please cite this publication when using this code:
% H. Leutheuser, S. Gradl, L. Anneken, M. Arnold, N. Lang, S. Achenbach, B.
% M. Eskofier, "Instantaneous P- and T-wave detection: assessment of three
% ECG fiducial points detection algorithms", submitted, 2016.
%
% MIT License
%
% Copyright (c) 2016 Heike Leutheuser
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
% copies of the Software, and to permit persons to whom the Software is
% furnished to do so, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in all
% copies or substantial portions of the Software.
%
% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
% IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
% AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
% OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
% SOFTWARE.
%% Edited by Hooman Sedghamiz, Sep, 2018
% TODO: Clean up --> Preallocation MaxPeakPosition

MaxPeakPosition = [];
%initialize consecutive numbering of found wave events
ii = 1;


%adapt all values to sampling frequency of 250 Hz -> filter coefficients
%can only be used for a sampling rate of 250 Hz
if (fs ~= 250)
%downsampling of signal
signal = resample_new(signal,250,fs);

%adaption of searchWindow to sampling rate of 250 Hz
% orgSearchWindow = searchWindow;
%searchWindow.rPeaks = round(searchWindow.rPeaks/fs*250);
%searchWindow.start = round(searchWindow.start(:,1)/fs*250);
%searchWindow.end = round(searchWindow.end(:,1)/fs*250);

end

%% ================ Use Pan-Tompkin's to locate R waves ============= %%
[~,R_ind]= pan_tompkin(signal,fs,0);
searchWindow = struct;
searchWindow.rPeaks(:,1) = R_ind(1:end-1);
searchWindow.rPeaks(:,2) = R_ind(2:end);

for i = 1:max(size(searchWindow.rPeaks))

%get searchWindow for sigPart -> RR interval
startR = searchWindow.rPeaks(i,1);
stopR = searchWindow.rPeaks(i,2);
sigRR = signal(startR:stopR);

%get search Window for wave event
start = startR + round(0.060*fs); % Begin search 60msec after R
stop = stopR - round(0.100*fs); % Begin search 100msec prior to R

%get rel Positions (search Window) for search within sigRR
relStart = start - (startR-1);
if relStart <= 0
relStart = 1;
end
relStop = stop - (startR-1);

%initial assumption: wave can be detected in the 4th scale
scale = 4; %initialize scale
while (scale <= 5) %scale

%apply Wavelet Transform
coefsWTRR = algorithmeATrous (sigRR, scale);
coefsWT = coefsWTRR(relStart:relStop);

%for scale 3
coefsWTRR3 = algorithmeATrous (sigRR, 3);
coefsWT3 = coefsWTRR3 (relStart:relStop);

%calculate threshold eta
%get RMS value of the whole signal
if (strcmpi(wave,'P'))
epsilon = 0.02*rootMeanSquare(coefsWTRR);
elseif (strcmpi(wave,'T'))
epsilon = 0.25*rootMeanSquare(coefsWTRR);
end

% Search for local maxima and minima in scale j
l = 1;
PosPeaktemp = double.empty;
for k=2:max(size(coefsWT))-1
if (coefsWT(k-1) >= coefsWT(k) && coefsWT(k) <= coefsWT(k+1) ...
|| coefsWT(k-1) <= coefsWT(k) && coefsWT(k) >= coefsWT(k+1))
PosPeaktemp(l) = k;
l = l+1;
end
end
clear l k

%comparison to threshold epsilon -> identification if wave is present
if ~isempty(PosPeaktemp) && length(PosPeaktemp) > 1
PosPeak = double.empty;
valPosPeaktemp = abs(coefsWT(PosPeaktemp)); %values
relPosPeaktemp = valPosPeaktemp > epsilon; %relevant
PosPeak = PosPeaktemp(relPosPeaktemp);
else
scale = scale+1;
clear PosPeaktemp PosPeak coefsWTRR coefsWT
continue;
end
clear relPosPeaktemp valPosPeaktemp PosPeaktemp

% Calculate threshold gamma
if (strcmpi(wave,'P'))
gamma = 0.125*max(abs(coefsWT));
elseif (strcmpi(wave,'T'))
gamma = 0.125*max(abs(coefsWT));
end

%comparison to threshold gamma -> identification if significant wave is
%present
if ~isempty(PosPeak) && length(PosPeak) > 1
% Search modulus maxima which is higher than gamma
PeakMod = double.empty;
valPeakModtemp = abs(coefsWT(PosPeak)); %values
relPeakModtemp = valPeakModtemp > gamma; %relevant
PeakMod = PosPeak(relPeakModtemp);
clear valPeakModtemp relPeakModtemp PosPeak

%check if zero crossing exists
if ~isempty(PeakMod) && length(PeakMod) > 1
%check for opposite signs
signPeakMod = sign(coefsWT(PeakMod));
ind2Remove = [];
for j = 1:size(signPeakMod,1)-1
tmp = sum(signPeakMod(j:j+1));
if (tmp ~= 0)
ind2Remove = [ind2Remove; j+1];
end
clear tmp
end
PeakMod(ind2Remove) = [];
clear j
end

if ~isempty(PeakMod) && (length(PeakMod) > 1)

%look for zero crossings in scale 3; only 1 should exist
PeakPos = double.empty;
tmpSign = sign(coefsWT3(PeakMod(1):PeakMod(end)));
for j = 1:max(size(tmpSign))-1
tmp = sum(tmpSign(j:j+1));
if tmp == 0
PeakPos = [PeakPos;j;];
end
clear tmp
end
clear j

if isempty(PeakPos)
clear tmpSign
tmpSign = sign(coefsWT(PeakMod(1):PeakMod(end)));

clear PeakPos
PeakPos = double.empty;
for j = 1:max(size(tmpSign))-1
tmp = sum(tmpSign(j:j+1));
if tmp == 0 %zero crossing
PeakPos = [PeakPos;j;];
end
clear tmp
end
clear j
end

if max(size(PeakPos)) > 1
%if more than one PeakPos was found --> take average;
%assumption of biphasic waves
orgPeakPos = PeakPos;
clear PeakPos
PeakPos = round(mean(orgPeakPos));
clear orgPeakPos
end

if ~isempty(PeakPos)
MaxPeakPosition(ii,1) = startR + PeakPos -1 + ...
relStart + PeakMod(1);
ii = ii+1;

scale = 6;
else
scale = scale+1;
continue;
end
else
scale = scale+1;
continue;
end
else
scale = scale+1;
continue;
end
clear PeakMod PeakPos gamma PosPeaktemp
end
clear sigPart scale
clear coefsWT coefsWT3 coefsWTRR coefsWTRR3 epsilon relStart relStop ...
scale start startR stop stopR tmpSign
end

PeakPositions.MaxPeak = round(MaxPeakPosition*fs/250);

%nested function
function [rmsVal] = rootMeanSquare(inputVector)
squaredInput = inputVector.^2;
rmsVal = sqrt(nanmean( squaredInput));
end

%nested function
function [resampled_data] = resample_new(original_data,fs_adapted,...
fs_original)
time = length(original_data)/fs_original;
t = (1:1:length(original_data))'/fs_original;
t_adapted = (1:1:time*fs_adapted)/fs_adapted;

resampled_data = interp1(t,original_data,t_adapted, 'linear');
end
end
120 changes: 120 additions & 0 deletions Algorithms/algorithmeATrous.m
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function [filteredSig] = algorithmeATrous (signal, scale)
%ALGORITHMEATROUS applies the algorithme ? trous implementation as
%suggested by Martinez et al. (2004): A Wavelet-based ECG delineator:
%evaluation on standard databases to the signal up to indented scale.
%filteredSig is the high-pass part of the signal at the chosen scale.
%
% Inputs:
% signal - 1-dimenionsal signal.
% scale - intented scale of algorithme a trous; scale up to 5 can
% be realised.
%
% Output:
% filteredSig - high-pass part of the signal at the chosen scale
%
% BY Heike Leutheuser, 31.03.2016, heike.leutheuser@fau.de
%
% Please cite this publication when using this code:
% H. Leutheuser, S. Gradl, L. Anneken, M. Arnold, N. Lang, S. Achenbach, B.
% M. Eskofier, "Instantaneous P- and T-wave detection: assessment of three
% ECG fiducial points detection algorithms", submitted, 2016.
%
% MIT License
%
% Copyright (c) 2016 Heike Leutheuser
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
% copies of the Software, and to permit persons to whom the Software is
% furnished to do so, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in all
% copies or substantial portions of the Software.
%
% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
% IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
% AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
% OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
% SOFTWARE.


for i = 1:scale
switch i
case 1
filteredSig = signal;
%filtering with G(z)
bG = 2*[1 -1];
% delayG = 1; %n -> n-1

%filtering with H(z)
%used in the following
hSigTmp = signal;
bH = 1/8*[1 3 3 1];
% delayH = 2;
case 2
%filtering with G(z^2)
bG = 2*[1 0 -1];
% delayG = 2; %n -> n-2

%filtering with H(z^2)
bH = 1/8*[1 0 3 0 3 0 1];
% delayH = 4;
case 3
%filtering with G(z^4);
bG = 2*[1 0 0 0 -1];

%filtering with H(z^4);
bH = 1/8*[1 0 0 0 3 0 0 0 3 0 0 0 1];
case 4
%filtering with G(z^8)
bG = 2*[1 zeros(1,7) -1];

%filtering with H(z^8)
bH = 1/8*[1 zeros(1,7) 3 zeros(1,7) 3 zeros(1,7) 1];
case 5
%filtering with G(z^16)
bG = 2*[1 zeros(1,15) -1];
end

aG = 1;
aH = 1;

delayG = 2^(scale-1);
delayH = 2^((scale-1)-1);

%filtering with G
filteredSig = modFilter(bG,aG,hSigTmp,delayG);
clear bG aG delayG

if (scale < 5)
%filtering with H
hSigTmp = modFilter(bH,aH,hSigTmp,delayH);
clear bH aH delayH
end


end


%nested function with filtering
function sigOutput = modFilter(bVar,aVar,sigInput,delayVar)
sigOutput = filter(bVar,aVar,sigInput);

%considering the "Einschwingzeit" -> length of filter coefficients - 1
[~,m] = size(bVar);
sigOutput(1:m-1,1) = NaN;
sigOutput(end-(m-1)+1:end,1) = NaN;
clear m

%adapt signal according to delay (introduced by the filtering itself)
[n,~] = size(sigInput);
sigOutput(1:delayVar) = [];
sigOutput(n-delayVar+1:n,1) = NaN;
clear n
end

end
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