clear all
clc
tic
addr = '.\mitbihdb';
Files=dir(strcat(addr,'\*.mat'));
%% Translate PhysioNet classification results to AAMI and AAMI2 labling schemes
% AAMI Classes:
% % N = N, L, R, e, j
% % S = A, a, J, S
% % V = V, E
% % F = F
% % Q = /, f, Q
% AAMI2 Classes:
% % N = N, L, R, e, j
% % S = A, a, J, S
% % V = V, E, F
% % Q = /, f, Q
% https://github.com/ehendryx/deim-cur-ecg/blob/master/DS1_MIT_CUR_beat_classification.m
AAMI_annotations = {'N' 'S' 'V' 'F' 'Q'};
AAMI2_annotations = {'N' 'S' 'V_hat' 'Q'};
DS1=[101, 106, 108, 109, 112, 114, 115, 116, 118,119, 122, 124, 201, 203, 205, 207, 208, 209, 215, 220, 223,230];
DS2 =[100, 103, 105, 111, 113, 117, 121, 123,200, 202, 210, 212, 213, 214, 219, 221, 222, 228, 231, 232, 233,234];
index = 1;
beat_len = 280;
n_cycles = 0;
featuresSeg = [];
groupN = [];
groupV = [];
groupS = [];
groupF = [];
groupQ = [];
for j=1:2
N_class = 0;V_class=0;F_class=0;Q_class=0;S_class=0;
if j==1
DS = DS1;
else
DS= DS2;
end
for i=1:length(DS) % Files names3signals
%% load the files
% load ('100m.mat') % the signal will be loaded to "val" matrix
% val = (val - 1024)/200; % you have to remove "base" and "gain"
% ECGsignal = val(1,1:1000); % select the lead (Lead I)
% Fs = 360; % sampling frequecy
% t = (0:length(ECGsignal)-1)/Fs; % time
% plot(t,ECGsignal)
%
[pathstr,name,ext] = fileparts(strcat(num2str (DS(i)),'m'));
nsig = 1;
[tm,ecgsig,ann,Fs,sizeEcgSig,timeEcgSig] = loadEcgSig([addr filesep name]);
signal = ecgsig(nsig,:);
%%
% rPeaks = rDetection(signal, Fs);
% rPeaks = get_rpeaks(signal, Fs);
rPeaks = cell2mat(ann(3))+1;
n_cycles = n_cycles + length(rPeaks);
% [R_i,R_amp,S_i,S_amp,T_i,T_amp,Q_i,Q_amp] = peakdetect(signal,Fs);
% rPeaks = R_i;
rPeaks = double(rPeaks);
peaks = qsPeaks(signal, rPeaks, Fs);
tpeaks = peaks(:,7);
% %% Plot P Q R S T points
% N = length(signal);
% tm = 1/Fs:1/Fs:N/Fs;
% figure;plot(tm,signal);hold on
% scatter(peaks(:,1)/Fs,signal(peaks(:,1)),'g*') % P points
% scatter(peaks(:,3)/Fs,signal(peaks(:,3)),'k+') % Q points
% scatter(peaks(:,4)/Fs,signal(peaks(:,4)),'ro') % R points
% scatter(peaks(:,5)/Fs,signal(peaks(:,5)),'c^') % S points
% scatter(peaks(:,7)/Fs,signal(peaks(:,7)),'mo') % T points
% xlabel('Seconds'); ylabel('Amplitude')
% title('ECG peaks detection')
% legend('Raw signal','P','Q','R','S','T')
% hold off
%
%% grouping
% gourp 0: N(normal and bundle branch block beats); group 2: V(ventricular
%ectopic beats); group 1: S(supraventricular ectopic beats); group 3: F (fusion of N and V beats)
% group Q:4 unknown beat
% consider just absolute features, where each row of extraxted features is
% related to one segment
annots_list = ['N','L','R','e','j','S','A','a','J','V','E','F','/','f','Q'];
annot = cell2mat(ann(4));
indices = ismember(rPeaks,peaks(:,4));
annot = annot(indices);
% rps = peaks(:,4);
% AAMI Classes:
% % N = N, L, R, e, j
% % S = A, a, J, S
% % V = V, E
% % F = F
% % Q = /, f, Q
seg_values = {};
seg_labels =[];
ind_seg = 1;
% normalize
signal = normalize(signal);
for ind=1:length(annot)
if ~ismember(annot(ind),annots_list)
continue;
end
N_g = ['N', 'L', 'R', 'e', 'j'];%0
S_g = ['A', 'a', 'J', 'S'];%1
V_g = ['V', 'E'];%2
F_g = ['F'];%3
Q_g = [' /', 'f', 'Q'];%4
if(ismember(annot(ind),N_g))
lebel = 'N';
% if(N_class >8031) %(N_class >8031)
% continue
% end
elseif(ismember(annot(ind),S_g))
lebel = 'S';
elseif(ismember(annot(ind),V_g))
lebel = 'V';
elseif(ismember(annot(ind),F_g))
lebel = 'F';
elseif(ismember(annot(ind),Q_g))
lebel = 'Q';
else
throw("No label! :(")
end
if ind==1
seg_values{ind_seg} = signal(1:tpeaks(ind)-1)';
t_sig = imresize(seg_values{ind_seg}(1:min(Fs,length(seg_values{ind_seg}))), [beat_len 1]);
seg_values{ind_seg} = t_sig;
seg_labels(ind_seg) = lebel;
% plot(cell2mat(seg_values(ind_seg)))
ind_seg = ind_seg+1;
continue;
end
t_sig = imresize(signal(tpeaks(ind-1):tpeaks(ind)-1)', [beat_len 1]);
seg_values{ind_seg} =t_sig ;
% figure;
% plot(cell2mat(seg_values(ind_seg)))
% determine the label
seg_labels(ind_seg) = lebel;
ind_seg = ind_seg+1;
end
if j==1
s2s_mitbih_DS1(i).seg_values = seg_values';
s2s_mitbih_DS1(i).seg_labels = char(seg_labels);
else
s2s_mitbih_DS2(i).seg_values = seg_values';
s2s_mitbih_DS2(i).seg_labels = char(seg_labels);
end
% featuresSeg = [featuresSeg; peakSegFeats(N_inds,:),repmat(0,length(N_inds),1)];
% group N:0
% N = N, L, R, e, j
N_inds = find(annot=='N');
N_inds = [N_inds;find(annot=='L')];
N_inds = [N_inds;find(annot=='R')];
N_inds = [N_inds;find(annot=='e')];
N_inds = [N_inds;find(annot=='j')];
N_class = N_class + length(N_inds);
% group S:1
% S = A, a, J, S
S_inds = find(annot=='S');
S_inds = [S_inds;find(annot=='A')];
S_inds = [S_inds;find(annot=='a')];
S_inds = [S_inds;find(annot=='J')];
S_class = S_class + length(S_inds);
% group V:2
% V = V, E
V_inds = find(annot=='V');
V_inds = [V_inds;find(annot=='E')];
V_class = V_class + length(V_inds);
% featuresSeg = [featuresSeg; peakSegFeats(V_inds,:),repmat(2,length(V_inds),1)];
% group F:3
% F = F
F_inds = find(annot=='F');
F_class = F_class + length(F_inds);
% group Q:4
% Q = /, f, Q
Q_inds = find(annot=='/');
Q_inds = [Q_inds;find(annot=='f')];
Q_inds = [Q_inds;find(annot=='Q')];
Q_class = Q_class + length(Q_inds);
end
F_class
N_class
Q_class
S_class
V_class
F_class+N_class+Q_class+S_class+V_class
end
% % calucualte the mean length of all beats in the dataset: it is 280
% sizes = [];
% for ind=1:length(s2s_mitbih)
% sizes= [sizes;cellfun(@length,s2s_mitbih(ind).seg_values)];
% end
% beat_len = floor(mean(sizes))
save s2s_mitbih_aami_DS1DS2.mat s2s_mitbih_DS1 s2s_mitbih_DS2
toc
disp('Successfully generated :)')
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