Unable to find file , load file to xl file .
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Md. Mohidul Islam
2023-1-13
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1 个评论
Stephen23
2023-1-14
编辑:Stephen23
2023-1-14
Do NOT store user files in the installation directory of any application!
'C:\Program Files\MATLAB\R2022b\bin\my_excel_file.xls' % !!! DO NOT use this location !!!
Your Windows should protect the installation folder, so whatever you are doing ... is best avoided.
回答(1 个)
Voss
2023-1-13
Two things:
1) You're using
y1 = xlsread('F:\matlab\bin_my_excel_file');
% ^ underscore
but 'bin' is a directory and the file is (supposedly) 'my_excel_file', so that should be
y = xlsread('F:\matlab\bin\my_excel_file');
% ^ backslash
2) Specify the file name completely, including the extension, either .csv or .xls. I don't know which one you mean because they both exist.
% which file?
% this one:
y1 = xlsread('F:\matlab\bin\my_excel_file.csv')
% or this one:
y1 = xlsread('F:\matlab\bin\my_excel_file.xls')
Also, readmatrix, readtable, and readcell are recommended over xlsread in R2022b.
You can pick the one that makes the most sense for the class of the variable that should represent the contents of the file (a matrix, a table, or a cell array, respectively).
4 个评论
Md. Mohidul Islam
2023-1-13
移动:Voss
2023-1-13
close all;
clear all;
clc;
%%Select a filename in .mat format and load the file.
%[fname path]=uigetfile('*.mat');
%fname=strcat(path,fname);
%y1 = load(fname );
%file =load('I:\BIOM_Signal_processing\Hw5\ECGsignal_1.mat')
load('118e12m.mat')
disp('Contents of workspace after loading file:')
Contents of workspace after loading file:
whos
Name Size Bytes Class Attributes
val 1x3600 28800 double
fs = 250; % find the sampling rate or frequency
y1=xlsread('C:\Program Files\MATLAB\R2022b\bin\my_excel_file.xls');
Error using xlsread
Unable to open file 'C:\Program Files\MATLAB\R2022b\bin\my_excel_file.xls'.
File 'C:\Program Files\MATLAB\R2022b\bin\my_excel_file.xls' not found.
Unable to open file 'C:\Program Files\MATLAB\R2022b\bin\my_excel_file.xls'.
File 'C:\Program Files\MATLAB\R2022b\bin\my_excel_file.xls' not found.
T = 1/fs;% sampling rate or frequency
% find the length of the data per second
N = length(y1);
ls = size(y1);
t = (0 : N-1) / fs;% sampling period
%t = (0 : N-1) *T;
%t = (0:1:length(y1)-1)/fs;
%subplot (2,2,2)
%plot (t,data);
figure; %subplot(1,2,1);
plot(t,y1);
%plot(x,y2, 'g');
title ('plot of the original of ECG signal')
xlabel ('time (sec)')
ylabel ('Amplitute (mv)')
grid on;
y1_n=(y1-min(y1))/(max(y1)-min(y1)); % normalize between 0-1
fnyquist = fs/2;
%% find P
m1=max(y1)*.60;
P=find(y1>=m1);
y1_1500 = y1(1:1850);
t2 = 1:length(y1_1500);
figure;
plot(t2,y1_1500);
title ('plot of subset of the ECG signal')
xlabel ('time (msec)')
ylabel ('Amplitute (mv)')
grid on
%% used the snip code from this website.
%%%%http://www.mathworks.com/help/signal/examples/peak-analysis.html
%Detrending Data
%The above signal shows a baseline shift and therefore does not represent the true amplitude. In order to remove the trend, fit a low order polynomial to the signal and use the polynomial to detrend it.
[p,s,mu] = polyfit((1:numel(y1_1500))',y1_1500,6);
f_y = polyval(p,(1:numel(y1_1500))',[],mu);
ECG_data = y1_1500 - f_y; % Detrend data
N1= length (y1_1500);
t1 = (0 : N1-1) / fs;% sampling period
figure
%plot(t1,ECG_data); grid on
plot(t2,ECG_data); grid on
ax = axis; axis([ax(1:2) -2.2 2.2])
%ax = axis; axis([ax(1:2) -3.2 3.2])
title('Detrended ECG Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
legend('Detrended ECG Signal')
%Thresholding to Find Peaks of Interest
%The QRS-complex consists of three major components: Q-wave, R-wave, S-wave. The R-waves can be detected by thresholding peaks above 0.5mV. Notice that the R-waves are separated by more than 200 samples. Use this information to remove unwanted peaks by specifying a 'MinPeakDistance'.
[~,locs_Rwave] = findpeaks(ECG_data,'MinPeakHeight',0.5,...
'MinPeakDistance',120);
%Finding Local Minima in Signal
%Local minima can be detected by finding peaks on an inverted version of the original signal.
ECG_inverted = -ECG_data;
[~,locs_Swave] = findpeaks(ECG_inverted,'MinPeakHeight',0.4,...
'MinPeakDistance',120);
%The following plot shows the R-waves and S-waves detected in the signal.
figure
hold on
plot(t2,ECG_data);
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
%axis([0 1850 -1.1 1.1]); grid on;
axis([0 1850 -2.2 2.2]); grid on;
legend('ECG Signal','R-waves','S-waves');
xlabel('time msec'); ylabel('Voltage(mV)')
title('R-wave and S-wave in ECG Signal')
[~,locs_Twave] = findpeaks(ECG_data,'MinPeakHeight',-0.02,...
'MinPeakDistance',50);
figure;
hold on
plot(t2,ECG_data);
plot(locs_Twave,ECG_data(locs_Twave),'X','MarkerFaceColor','y');
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks in Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('ECG signal','T-wave','R-wave','S-wave');
[~,locs_Pwave] = findpeaks(ECG_data,'MinPeakHeight',-0.09,...
'MinPeakDistance',25);
figure;
hold on
plot(t2,ECG_data);
plot(locs_Pwave,ECG_data(locs_Pwave),'x','MarkerFaceColor','y');
plot(locs_Twave,ECG_data(locs_Twave),'X','MarkerFaceColor','g');
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks in Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('ECG signal','P-wave','T-wave','R-wave','S-wave');
[~,locs_qwave] = findpeaks(ECG_data,'MinPeakHeight',-0.2);
figure;
hold on
plot(t2,ECG_data);
plot(locs_qwave,ECG_data(locs_qwave),'x','MarkerFaceColor','y');
% link and zoom in to show the changes
%linkaxes(ax(1:2),'xy');
%axis(ax,[60 230 0.006 -0.04])
%Next, we try and determine the locations of the Q-waves. Thresholding the peaks to locate the Q-waves results in detection of unwanted peaks as the Q-waves are buried in noise. We filter the signal first and then find the peaks. Savitzky-Golay filtering is used to remove noise in the signal.
smoothECG = sgolayfilt(ECG_data,1,3);
figure
plot(t2,ECG_data,'b',t2,smoothECG,'r'); grid on
axis tight;
xlabel('time msec'); ylabel('Voltage(mV)');
legend('ECG Signal','Filtered Signal')
title('Filtering Noisy ECG Signal')
%We perform peak detection on the smooth signal and use logical indexing to find the locations of the Q-waves.
%[~,min_locs] = findpeaks(-smoothECG,'MinPeakDistance',29);
%[~,min_locs] = findpeaks(smoothECG,'MinPeakDistance',2);%Twave
[~,min_locs] = findpeaks(smoothECG,'MinPeakDistance',50);
% Peaks between -0.2mV and -0.5mV
%locs_Qwave = min_locs(smoothECG(min_locs)>-0.3 &
%-smoothECG(min_locs)<-0.1); %Twave
locs_Qwave = min_locs(smoothECG(min_locs)>-0.3 & -smoothECG(min_locs)<-0.11);
figure
hold on
plot(t2,smoothECG);
plot(locs_Qwave,smoothECG(locs_Qwave),'rs','MarkerFaceColor','g');
plot(locs_Rwave,smoothECG(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,smoothECG(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks in Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('Smooth ECG signal','T-interval','R-wave','S-wave');
%The above figure shows that the QRS-complex successfully detected in the noisy ECG signal.
%Error Between Noisy and Smooth Signal
%Notice the average difference between the QRS-complex in the raw and the detrended filtered signal.
% Values of the Extrema
[val_Qwave, val_Rwave, val_Swave] = deal(smoothECG(locs_Qwave), smoothECG(locs_Rwave), smoothECG(locs_Swave));
meanError_Qwave = mean((y1_1500(locs_Qwave) - val_Qwave))
meanError_Rwave = mean((y1_1500(locs_Rwave) - val_Rwave))
meanError_Swave = mean((y1_1500(locs_Swave) - val_Swave))
%% find PP interval
i = 0; %% to make the code start from 0.
rr = 0; %% each time the code run, rr distance two peaks
hold off % for the next graph
rrinterval = zeros(3600,1); % create an array to strore 2 peaks
beat_count =0;
for k = 2 : length(y1)-1
%the peak has to be greater than 1 and greater than the value before it and greater then the value after it.
if(y1(k)> y1(k-1) && y1(k) > y1(k+1) && y1(k)> 1);
beat_count = beat_count +1;
if beat_count ==1;
rr =0;
else
rr = k-i;
rrinterval(k)=rr;
i=k;
end
else
rrinterval(k)= rr;
end
end
figure;
plot (rrinterval);
xlabel('Time in sec*10^-2'), ylabel('Distance betweeen 2 Heatbeats (R-R) in sec*10^-2'), title('R-R intervals');
%% find PP interval
%% heart rate analysis
% count the dominat peak
beat_count =0;
for k = 2 : length(y1)-1
%the peak has to be greater than 1 and greater than the value before it and greater then the value after it.
if(y1(k)> y1(k-1) && y1(k) > y1(k+1) && y1(k)> 1)
beat_count = beat_count +1;
end
end
display (k);
disp('dominant peaks');
%% divide the peak count by the duration in minute
duration_in_sec = N/fs;
duration_in_minute = duration_in_sec/60;
BPM = beat_count/duration_in_minute;
%%% DFT to describe the signal in the frequency
NFFT = 2 ^ nextpow2(N);
Y = fft(y1, NFFT) / N;
f = (fs / 2 * linspace(0, 1, NFFT / 2+1))'; % Vector containing frequencies in Hz
amp = ( 2 * abs(Y(1: NFFT / 2+1))); % Vector containing corresponding amplitudes
figure;
plot (f, amp);
title ('plot single-sided amplitude spectrume of the ECG signal')
xlabel ('frequency (Hz)')
ylabel ('|y(f)|')
grid on;
max_value=max(y1);
mean_value=mean(y1);
threshold=(max_value-mean_value)/2;
%% downsampling ½ sample frequency
close all;
clear all;
clc;
%%Select a filename in .mat format and load the file.
%[fname path]=uigetfile('*.mat');
%fname=strcat(path,fname);
%y1 = load(fname );
%file =load('I:\BIOM_Signal_processing\Hw5\ECGsignal_1.mat')
load('I:\BIOM_Signal_processing\Hw5\ECGsignal_1.mat')
disp('Contents of workspace after loading file:')
whos
fs = 250; % find the sampling rate or frequency
fs2 = 250*1/2;
y1=xlsread('I:\BIOM_Signal_processing\Hw5\ECGsignal_1.xls');
T = 1/fs;% sampling rate or frequency
% find the length of the data per second
N = length(y1);
ls = size(y1);
t = (0 : N-1) / fs;% sampling period
%t = (0 : N-1) *T;
%t = (0:1:length(y1)-1)/fs;
%subplot (2,2,2)
%plot (t,data);
figure; %subplot(1,2,1);
plot(t,y1);
%plot(x,y2, 'g');
title ('plot of the original of ECG signal')
xlabel ('time (sec)')
ylabel ('Amplitute (mv)')
grid on;
%%%%%%%%%%%%%
% down sampling 1/2 of frequency sample
y2 = resample(y1,fs2,fs);
N2 = length(y2);
ls2 = size(y2);
t22 = (0 : N2-1) / fs2;% sampling period
figure; %subplot(1,2,1);
plot(t22,y2);
title ('plot of the down sampling 1/2 frequency sample of ECG signal')
xlabel ('time (sec)')
ylabel ('Amplitute (mv)')
grid on;
%y1_n=(y1-min(y1))/(max(y1)-min(y1)); % normalize between 0-1
fnyquist = fs2/2;
%% find P
m1=max(y2)*.60;
P=find(y2>=m1);
y1_1500 = y2(1:1850);
t2 = 1:length(y1_1500);
figure;
plot(t2,y1_1500);
title ('plot of subset of down sampling 1/2 frequency sample the ECG signal')
xlabel ('time (msec)')
ylabel ('Amplitute (mv)')
grid on
%% used the snip code from this website.
%%%%http://www.mathworks.com/help/signal/examples/peak-analysis.html
%Detrending Data
%The above signal shows a baseline shift and therefore does not represent the true amplitude. In order to remove the trend, fit a low order polynomial to the signal and use the polynomial to detrend it.
[p,s,mu] = polyfit((1:numel(y1_1500))',y1_1500,6);
f_y = polyval(p,(1:numel(y1_1500))',[],mu);
ECG_data = y1_1500 - f_y; % Detrend data
N1= length (y1_1500);
t1 = (0 : N1-1) / fs2;% sampling period
figure
%plot(t1,ECG_data); grid on
plot(t2,ECG_data); grid on
ax = axis; axis([ax(1:2) -2.2 2.2])
%ax = axis; axis([ax(1:2) -3.2 3.2])
title('Detrended down sampling 1/2 frequency sample ECG Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
legend('Detrended ECG Signal')
%Thresholding to Find Peaks of Interest
%The QRS-complex consists of three major components: Q-wave, R-wave, S-wave. The R-waves can be detected by thresholding peaks above 0.5mV. Notice that the R-waves are separated by more than 200 samples. Use this information to remove unwanted peaks by specifying a 'MinPeakDistance'.
[~,locs_Rwave] = findpeaks(ECG_data,'MinPeakHeight',0.5,...
'MinPeakDistance',60);
%Finding Local Minima in Signal
%Local minima can be detected by finding peaks on an inverted version of the original signal.
ECG_inverted = -ECG_data;
[~,locs_Swave] = findpeaks(ECG_inverted,'MinPeakHeight',0.4,...
'MinPeakDistance',60);
%The following plot shows the R-waves and S-waves detected in the signal.
figure
hold on
plot(t2,ECG_data);
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
%axis([0 1850 -1.1 1.1]); grid on;
axis([0 1850 -2.2 2.2]); grid on;
legend('ECG Signal','R-waves','S-waves');
xlabel('time msec'); ylabel('Voltage(mV)')
title('R-wave and S-wave in down sampling 1/2 frequency sample of ECG Signal')
[~,locs_Twave] = findpeaks(ECG_data,'MinPeakHeight',-0.02,...
'MinPeakDistance',25);
figure;
hold on
plot(t2,ECG_data);
plot(locs_Twave,ECG_data(locs_Twave),'X','MarkerFaceColor','y');
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks in down sampling 1/2 frequency sample Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('ECG signal','T-wave','R-wave','S-wave');
[~,locs_Pwave] = findpeaks(ECG_data,'MinPeakHeight',-0.09,...
'MinPeakDistance',12);
figure;
hold on
plot(t2,ECG_data);
plot(locs_Pwave,ECG_data(locs_Pwave),'x','MarkerFaceColor','y');
plot(locs_Twave,ECG_data(locs_Twave),'X','MarkerFaceColor','g');
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks in down sampling 1/2 frequency sample Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('ECG signal','P-wave','T-wave','R-wave','S-wave');
[~,locs_qwave] = findpeaks(ECG_data,'MinPeakHeight',-0.2);
figure;
hold on
plot(t2,ECG_data);
plot(locs_qwave,ECG_data(locs_qwave),'x','MarkerFaceColor','y');
% link and zoom in to show the changes
%linkaxes(ax(1:2),'xy');
%axis(ax,[60 230 0.006 -0.04])
%Next, we try and determine the locations of the Q-waves. Thresholding the peaks to locate the Q-waves results in detection of unwanted peaks as the Q-waves are buried in noise. We filter the signal first and then find the peaks. Savitzky-Golay filtering is used to remove noise in the signal.
smoothECG = sgolayfilt(ECG_data,1,3);
figure
plot(t2,ECG_data,'b',t2,smoothECG,'r'); grid on
axis tight;
xlabel('time msec'); ylabel('Voltage(mV)');
legend('ECG Signal','Filtered Signal')
title('Filtering Noisy of down sampling 1/2 frequency sample ECG Signal')
%We perform peak detection on the smooth signal and use logical indexing to find the locations of the Q-waves.
%[~,min_locs] = findpeaks(-smoothECG,'MinPeakDistance',29);
%[~,min_locs] = findpeaks(smoothECG,'MinPeakDistance',2);%Twave
[~,min_locs] = findpeaks(smoothECG,'MinPeakDistance',25);
% Peaks between -0.2mV and -0.5mV
%locs_Qwave = min_locs(smoothECG(min_locs)>-0.3 &
%-smoothECG(min_locs)<-0.1); %Twave
locs_Qwave = min_locs(smoothECG(min_locs)>-0.3 & -smoothECG(min_locs)<-0.11);
figure
hold on
plot(t2,smoothECG);
plot(locs_Qwave,smoothECG(locs_Qwave),'rs','MarkerFaceColor','g');
plot(locs_Rwave,smoothECG(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,smoothECG(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks down sampling 1/2 frequency sample in Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('Smooth ECG signal','T-interval','R-wave','S-wave');
%The above figure shows that the QRS-complex successfully detected in the noisy ECG signal.
%Error Between Noisy and Smooth Signal
%Notice the average difference between the QRS-complex in the raw and the detrended filtered signal.
% Values of the Extrema
[val_Qwave, val_Rwave, val_Swave] = deal(smoothECG(locs_Qwave), smoothECG(locs_Rwave), smoothECG(locs_Swave));
meanError_Qwave = mean((y1_1500(locs_Qwave) - val_Qwave))
meanError_Rwave = mean((y1_1500(locs_Rwave) - val_Rwave))
meanError_Swave = mean((y1_1500(locs_Swave) - val_Swave))
%% find PP interval
i = 0; %% to make the code start from 0.
rr = 0; %% each time the code run, rr distance two peaks
hold off % for the next graph
rrinterval = zeros(3600,1); % create an array to strore 2 peaks
beat_count =0;
for k = 2 : length(y1)-1
%the peak has to be greater than 1 and greater than the value before it and greater then the value after it.
if(y1(k)> y1(k-1) && y1(k) > y1(k+1) && y1(k)> 1);
beat_count = beat_count +1;
if beat_count ==1;
rr =0;
else
rr = k-i;
rrinterval(k)=rr;
i=k;
end
else
rrinterval(k)= rr;
end
end
figure;
plot (rrinterval);
xlabel('Time in sec*10^-2'), ylabel('Distance betweeen 2 Heatbeats (R-R) in sec*10^-2'), title('R-R down sampling 1/2 frequency sample intervals');
%% find PP interval
%% heart rate analysis
% count the dominat peak
beat_count =0;
for k = 2 : length(y2)-1
%the peak has to be greater than 1 and greater than the value before it and greater then the value after it.
if(y2(k)> y2(k-1) && y2(k) > y2(k+1) && y2(k)> 1)
beat_count = beat_count +1;
end
end
display (k);
disp('dominant peaks');
%% divide the peak count by the duration in minute
duration_in_sec = N/fs2;
duration_in_minute = duration_in_sec/60;
BPM = beat_count/duration_in_minute;
%%% DFT to describe the signal in the frequency
NFFT = 2 ^ nextpow2(N2);
Y = fft(y2, NFFT) / N2;
f = (fs2 / 2 * linspace(0, 1, NFFT / 2+1))'; % Vector containing frequencies in Hz
amp = ( 2 * abs(Y(1: NFFT / 2+1))); % Vector containing corresponding amplitudes
figure;
plot (f, amp);
title ('plot single-sided amplitude spectrume of 1/2 frequency sample ECG signal')
xlabel ('frequency (Hz)')
ylabel ('|y(f)|')
grid on;
max_value=max(y1);
mean_value=mean(y1);
threshold=(max_value-mean_value)/2;
%%Downsampling ¼ sample frequency
close all;
clear all;
clc;
load('I:\BIOM_Signal_processing\Hw5\ECGsignal_1.mat')
disp('Contents of workspace after loading file:')
whos
fs = 250; % find the sampling rate or frequency
fs1 = 250*1/2;
fs2 = 250*1/4;
y1=xlsread('I:\BIOM_Signal_processing\Hw5\ECGsignal_1.xls');
T = 1/fs;% sampling rate or frequency
% find the length of the data per second
N = length(y1);
ls = size(y1);
t = (0 : N-1) / fs;% sampling period
figure; %subplot(1,2,1);
plot(t,y1);
%plot(x,y2, 'g');
title ('plot of the original of ECG signal')
xlabel ('time (sec)')
ylabel ('Amplitute (mv)')
grid on;
% down sampling 1/2 of frequency sample
y2a = resample(y1,fs1,fs);
N1 = length(y2a);
ls1 = size(y2a);
t21 = (0 : N1-1) / fs1;% sampling period
figure; %subplot(1,2,1);
plot(t21,y2a);
title ('plot of the down sampling 1/2 frequency sample of ECG signal')
xlabel ('time (sec)')
ylabel ('Amplitute (mv)')
grid on;
%%%%%%%%%%%%%
% down sampling 1/4 of frequency sample
y2 = resample(y1,63,250);
N2 = length(y2);
ls2 = size(y2);
t22 = (0 : N2-1) / fs2;% sampling period
figure; %subplot(1,2,1);
plot(t22,y2);
title ('plot of the down sampling 1/4 frequency sample of ECG signal')
xlabel ('time (sec)')
ylabel ('Amplitute (mv)')
grid on;
%% find P
m1=max(y2)*.60;
P=find(y2>=m1);
y1_1500 = y2(1:1850);
t2 = 1:length(y1_1500);
figure;
plot(t2,y1_1500);
title ('plot of subset of down sampling 1/4 frequency sample the ECG signal')
xlabel ('time (msec)')
ylabel ('Amplitute (mv)')
grid on
%% used the snip code from this website.
%%%%http://www.mathworks.com/help/signal/examples/peak-analysis.html
%Detrending Data
%The above signal shows a baseline shift and therefore does not represent the true amplitude. In order to remove the trend, fit a low order polynomial to the signal and use the polynomial to detrend it.
[p,s,mu] = polyfit((1:numel(y1_1500))',y1_1500,6);
f_y = polyval(p,(1:numel(y1_1500))',[],mu);
ECG_data = y1_1500 - f_y; % Detrend data
N1= length (y1_1500);
t1 = (0 : N1-1) / fs2;% sampling period
figure
%plot(t1,ECG_data); grid on
plot(t2,ECG_data); grid on
ax = axis; axis([ax(1:2) -2.2 2.2])
%ax = axis; axis([ax(1:2) -3.2 3.2])
title('Detrended down sampling 1/4 frequency sample ECG Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
legend('Detrended ECG Signal')
%Thresholding to Find Peaks of Interest
%The QRS-complex consists of three major components: Q-wave, R-wave, S-wave. The R-waves can be detected by thresholding peaks above 0.5mV. Notice that the R-waves are separated by more than 200 samples. Use this information to remove unwanted peaks by specifying a 'MinPeakDistance'.
[~,locs_Rwave] = findpeaks(ECG_data,'MinPeakHeight',0.5,...
'MinPeakDistance',30);
%Finding Local Minima in Signal
%Local minima can be detected by finding peaks on an inverted version of the original signal.
ECG_inverted = -ECG_data;
[~,locs_Swave] = findpeaks(ECG_inverted,'MinPeakHeight',0.4,...
'MinPeakDistance',30);
%The following plot shows the R-waves and S-waves detected in the signal.
figure
hold on
plot(t2,ECG_data);
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
%axis([0 1850 -1.1 1.1]); grid on;
axis([0 1850 -2.2 2.2]); grid on;
legend('ECG Signal','R-waves','S-waves');
xlabel('time msec'); ylabel('Voltage(mV)')
title('R-wave and S-wave in down sampling 1/4 frequency sample of ECG Signal')
[~,locs_Twave] = findpeaks(ECG_data,'MinPeakHeight',-0.02,...
'MinPeakDistance',13);
figure;
hold on
plot(t2,ECG_data);
plot(locs_Twave,ECG_data(locs_Twave),'X','MarkerFaceColor','y');
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks in down sampling 1/4 frequency sample Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('ECG signal','T-wave','R-wave','S-wave');
[~,locs_Pwave] = findpeaks(ECG_data,'MinPeakHeight',-0.09,...
'MinPeakDistance',6);
figure;
hold on
plot(t2,ECG_data);
plot(locs_Pwave,ECG_data(locs_Pwave),'x','MarkerFaceColor','y');
plot(locs_Twave,ECG_data(locs_Twave),'X','MarkerFaceColor','g');
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks in down sampling 1/4 frequency sample Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('ECG signal','P-wave','T-wave','R-wave','S-wave');
[~,locs_qwave] = findpeaks(ECG_data,'MinPeakHeight',-0.2);
figure;
hold on
plot(t2,ECG_data);
plot(locs_qwave,ECG_data(locs_qwave),'x','MarkerFaceColor','y');
% link and zoom in to show the changes
%linkaxes(ax(1:2),'xy');
%axis(ax,[60 230 0.006 -0.04])
%Next, we try and determine the locations of the Q-waves. Thresholding the peaks to locate the Q-waves results in detection of unwanted peaks as the Q-waves are buried in noise. We filter the signal first and then find the peaks. Savitzky-Golay filtering is used to remove noise in the signal.
smoothECG = sgolayfilt(ECG_data,1,3);
figure
plot(t2,ECG_data,'b',t2,smoothECG,'r'); grid on
axis tight;
xlabel('time msec'); ylabel('Voltage(mV)');
legend('ECG Signal','Filtered Signal')
title('Filtering Noisy of down sampling 1/4 frequency sample ECG Signal')
%We perform peak detection on the smooth signal and use logical indexing to find the locations of the Q-waves.
%[~,min_locs] = findpeaks(-smoothECG,'MinPeakDistance',29);
%[~,min_locs] = findpeaks(smoothECG,'MinPeakDistance',2);%Twave
[~,min_locs] = findpeaks(smoothECG,'MinPeakDistance',25);
% Peaks between -0.2mV and -0.5mV
%locs_Qwave = min_locs(smoothECG(min_locs)>-0.3 &
%-smoothECG(min_locs)<-0.1); %Twave
locs_Qwave = min_locs(smoothECG(min_locs)>-0.3 & -smoothECG(min_locs)<-0.11);
figure
hold on
plot(t2,smoothECG);
plot(locs_Qwave,smoothECG(locs_Qwave),'rs','MarkerFaceColor','g');
plot(locs_Rwave,smoothECG(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,smoothECG(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks down sampling 1/4 frequency sample in Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('Smooth ECG signal','T-interval','R-wave','S-wave');
%The above figure shows that the QRS-complex successfully detected in the noisy ECG signal.
%Error Between Noisy and Smooth Signal
%Notice the average difference between the QRS-complex in the raw and the detrended filtered signal.
% Values of the Extrema
[val_Qwave, val_Rwave, val_Swave] = deal(smoothECG(locs_Qwave), smoothECG(locs_Rwave), smoothECG(locs_Swave));
meanError_Qwave = mean((y1_1500(locs_Qwave) - val_Qwave))
meanError_Rwave = mean((y1_1500(locs_Rwave) - val_Rwave))
meanError_Swave = mean((y1_1500(locs_Swave) - val_Swave))
%% find PP interval
i = 0; %% to make the code start from 0.
rr = 0; %% each time the code run, rr distance two peaks
hold off % for the next graph
rrinterval = zeros(3600,1); % create an array to strore 2 peaks
beat_count =0;
for k = 2 : length(y1)-1
%the peak has to be greater than 1 and greater than the value before it and greater then the value after it.
if(y1(k)> y1(k-1) && y1(k) > y1(k+1) && y1(k)> 1);
beat_count = beat_count +1;
if beat_count ==1;
rr =0;
else
rr = k-i;
rrinterval(k)=rr;
i=k;
end
else
rrinterval(k)= rr;
end
end
figure;
plot (rrinterval);
xlabel('Time in sec*10^-2'), ylabel('Distance betweeen 2 Heatbeats (R-R) in sec*10^-2'), title('R-R down sampling 1/4 frequency sample intervals');
%% find PP interval
%% heart rate analysis
% count the dominat peak
beat_count =0;
for k = 2 : length(y2)-1
%the peak has to be greater than 1 and greater than the value before it and greater then the value after it.
if(y2(k)> y2(k-1) && y2(k) > y2(k+1) && y2(k)> 1)
beat_count = beat_count +1;
end
end
display (k);
disp('dominant peaks');
%% divide the peak count by the duration in minute
duration_in_sec = N/fs2;
duration_in_minute = duration_in_sec/60;
BPM = beat_count/duration_in_minute;
%%% DFT to describe the signal in the frequency
NFFT = 2 ^ nextpow2(N2);
Y = fft(y2, NFFT) / N2;
f = (fs2 / 2 * linspace(0, 1, NFFT / 2+1))'; % Vector containing frequencies in Hz
amp = ( 2 * abs(Y(1: NFFT / 2+1))); % Vector containing corresponding amplitudes
figure;
plot (f, amp);
title ('plot single-sided amplitude spectrume of 1/4 frequency sample ECG signal')
xlabel ('frequency (Hz)')
ylabel ('|y(f)|')
grid on;
max_value=max(y1);
mean_value=mean(y1);
threshold=(max_value-mean_value)/2;
%% upsampling 2 sample frequency
close all;
clear all;
clc;
load('I:\BIOM_Signal_processing\Hw5\ECGsignal_1.mat')
disp('Contents of workspace after loading file:')
whos
fs = 250; % find the sampling rate or frequency
fs2 = 250*2;
y1=xlsread('I:\BIOM_Signal_processing\Hw5\ECGsignal_1.xls');
T = 1/fs;% sampling rate or frequency
% find the length of the data per second
N = length(y1);
ls = size(y1);
t = (0 : N-1) / fs;% sampling period
figure; %subplot(1,2,1);
plot(t,y1);
%plot(x,y2, 'g');
title ('plot of the original of ECG signal')
xlabel ('time (sec)')
ylabel ('Amplitute (mv)')
grid on;
% up sampling 2 of frequency sample
y2 = resample(y1,500,250);
N2 = length(y2);
ls2 = size(y2);
t22 = (0 : N2-1) / fs2;% sampling period
figure; %subplot(1,2,1);
plot(t22,y2);
title ('plot of the up sampling 2 frequency sample of ECG signal')
xlabel ('time (sec)')
ylabel ('Amplitute (mv)')
grid on;
%% find P
m1=max(y2)*.60;
P=find(y2>=m1);
y1_1500 = y2(1:1850);
t2 = 1:length(y1_1500);
figure;
plot(t2,y1_1500);
title ('plot of subset of upsampling 2 frequency sample the ECG signal')
xlabel ('time (msec)')
ylabel ('Amplitute (mv)')
grid on
%% used the snip code from this website.
%%%%http://www.mathworks.com/help/signal/examples/peak-analysis.html
%Detrending Data
%The above signal shows a baseline shift and therefore does not represent the true amplitude. In order to remove the trend, fit a low order polynomial to the signal and use the polynomial to detrend it.
[p,s,mu] = polyfit((1:numel(y1_1500))',y1_1500,6);
f_y = polyval(p,(1:numel(y1_1500))',[],mu);
ECG_data = y1_1500 - f_y; % Detrend data
N1= length (y1_1500);
t1 = (0 : N1-1) / fs2;% sampling period
figure
%plot(t1,ECG_data); grid on
plot(t2,ECG_data); grid on
ax = axis; axis([ax(1:2) -2.2 2.2])
%ax = axis; axis([ax(1:2) -3.2 3.2])
title('Detrended upsampling 2 frequency sample ECG Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
legend('Detrended ECG Signal')
%Thresholding to Find Peaks of Interest
%The QRS-complex consists of three major components: Q-wave, R-wave, S-wave. The R-waves can be detected by thresholding peaks above 0.5mV. Notice that the R-waves are separated by more than 200 samples. Use this information to remove unwanted peaks by specifying a 'MinPeakDistance'.
[~,locs_Rwave] = findpeaks(ECG_data,'MinPeakHeight',0.5,...
'MinPeakDistance',240);
%Finding Local Minima in Signal
%Local minima can be detected by finding peaks on an inverted version of the original signal.
ECG_inverted = -ECG_data;
[~,locs_Swave] = findpeaks(ECG_inverted,'MinPeakHeight',0.4,...
'MinPeakDistance',240);
%The following plot shows the R-waves and S-waves detected in the signal.
figure
hold on
plot(t2,ECG_data);
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
%axis([0 1850 -1.1 1.1]); grid on;
axis([0 1850 -2.2 2.2]); grid on;
legend('ECG Signal','R-waves','S-waves');
xlabel('time msec'); ylabel('Voltage(mV)')
title('R-wave and S-wave in upsampling 2 frequency sample of ECG Signal')
[~,locs_Twave] = findpeaks(ECG_data,'MinPeakHeight',-0.02,...
'MinPeakDistance',100);
figure;
hold on
plot(t2,ECG_data);
plot(locs_Twave,ECG_data(locs_Twave),'X','MarkerFaceColor','y');
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks in upsampling 2 frequency sample Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('ECG signal','T-wave','R-wave','S-wave');
[~,locs_Pwave] = findpeaks(ECG_data,'MinPeakHeight',-0.09,...
'MinPeakDistance',52);
figure;
hold on
plot(t2,ECG_data);
plot(locs_Pwave,ECG_data(locs_Pwave),'x','MarkerFaceColor','y');
plot(locs_Twave,ECG_data(locs_Twave),'X','MarkerFaceColor','g');
plot(locs_Rwave,ECG_data(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,ECG_data(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks in upsampling 2 frequency sample Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('ECG signal','P-wave','T-wave','R-wave','S-wave');
[~,locs_qwave] = findpeaks(ECG_data,'MinPeakHeight',-0.2);
figure;
hold on
plot(t2,ECG_data);
plot(locs_qwave,ECG_data(locs_qwave),'x','MarkerFaceColor','y');
% link and zoom in to show the changes
%linkaxes(ax(1:2),'xy');
%axis(ax,[60 230 0.006 -0.04])
%Next, we try and determine the locations of the Q-waves. Thresholding the peaks to locate the Q-waves results in detection of unwanted peaks as the Q-waves are buried in noise. We filter the signal first and then find the peaks. Savitzky-Golay filtering is used to remove noise in the signal.
smoothECG = sgolayfilt(ECG_data,1,3);
figure
plot(t2,ECG_data,'b',t2,smoothECG,'r'); grid on
axis tight;
xlabel('time msec'); ylabel('Voltage(mV)');
legend('ECG Signal','Filtered Signal')
title('Filtering Noisy of upsampling 2 frequency sample ECG Signal')
%We perform peak detection on the smooth signal and use logical indexing to find the locations of the Q-waves.
%[~,min_locs] = findpeaks(-smoothECG,'MinPeakDistance',29);
%[~,min_locs] = findpeaks(smoothECG,'MinPeakDistance',2);%Twave
[~,min_locs] = findpeaks(smoothECG,'MinPeakDistance',25);
% Peaks between -0.2mV and -0.5mV
%locs_Qwave = min_locs(smoothECG(min_locs)>-0.3 &
%-smoothECG(min_locs)<-0.1); %Twave
locs_Qwave = min_locs(smoothECG(min_locs)>-0.3 & -smoothECG(min_locs)<-0.11);
figure
hold on
plot(t2,smoothECG);
plot(locs_Qwave,smoothECG(locs_Qwave),'rs','MarkerFaceColor','g');
plot(locs_Rwave,smoothECG(locs_Rwave),'rv','MarkerFaceColor','r');
plot(locs_Swave,smoothECG(locs_Swave),'rs','MarkerFaceColor','b');
grid on
title('Thresholding Peaks down sampling 2 frequency sample in Signal')
xlabel('time msec'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -2.2 2.2])
legend('Smooth ECG signal','T-wave','R-wave','S-wave');
%The above figure shows that the QRS-complex successfully detected in the noisy ECG signal.
%Error Between Noisy and Smooth Signal
%Notice the average difference between the QRS-complex in the raw and the detrended filtered signal.
% Values of the Extrema
[val_Qwave, val_Rwave, val_Swave] = deal(smoothECG(locs_Qwave), smoothECG(locs_Rwave), smoothECG(locs_Swave));
meanError_Qwave = mean((y1_1500(locs_Qwave) - val_Qwave))
meanError_Rwave = mean((y1_1500(locs_Rwave) - val_Rwave))
meanError_Swave = mean((y1_1500(locs_Swave) - val_Swave))
%% find PP interval
i = 0; %% to make the code start from 0.
rr = 0; %% each time the code run, rr distance two peaks
hold off % for the next graph
rrinterval = zeros(3600,1); % create an array to strore 2 peaks
beat_count =0;
for k = 2 : length(y1)-1
%the peak has to be greater than 1 and greater than the value before it and greater then the value after it.
if(y1(k)> y1(k-1) && y1(k) > y1(k+1) && y1(k)> 1);
beat_count = beat_count +1;
if beat_count ==1;
rr =0;
else
rr = k-i;
rrinterval(k)=rr;
i=k;
end
else
rrinterval(k)= rr;
end
end
figure;
plot (rrinterval);
xlabel('Time in sec*10^-2'), ylabel('Distance betweeen 2 Heatbeats (R-R) in sec*10^-2'), title('R-R down sampling 2 frequency sample intervals');
%% find PP interval
%% heart rate analysis
% count the dominat peak
beat_count =0;
for k = 2 : length(y2)-1
%the peak has to be greater than 1 and greater than the value before it and greater then the value after it.
if(y2(k)> y2(k-1) && y2(k) > y2(k+1) && y2(k)> 1)
beat_count = beat_count +1;
end
end
display (k);
disp('dominant peaks');
%% divide the peak count by the duration in minute
duration_in_sec = N/fs2;
duration_in_minute = duration_in_sec/60;
BPM = beat_count/duration_in_minute;
%%% DFT to describe the signal in the frequency
NFFT = 2 ^ nextpow2(N2);
Y = fft(y2, NFFT) / N2;
f = (fs2 / 2 * linspace(0, 1, NFFT / 2+1))'; % Vector containing frequencies in Hz
amp = ( 2 * abs(Y(1: NFFT / 2+1))); % Vector containing corresponding amplitudes
figure;
plot (f, amp);
title ('plot single-sided amplitude spectrume of upsampling 2 frequency sample ECG signal')
xlabel ('frequency (Hz)')
ylabel ('|y(f)|')
grid on;
max_value=max(y1);
mean_value=mean(y1);
threshold=(max_value-mean_value)/2;
Walter Roberson
2023-1-13
移动:Voss
2023-1-13
y1=xlsread('C:\Program Files\MATLAB\R2022b\bin\my_excel_file.xls');
We recommend against storing files in the MATLAB execution directory. You would need to be running with elevated access rights in order to be permtited to write files there.
Voss
2023-1-13
@Md. Mohidul Islam: Do you have another question about the code you posted? If you want someone to be able to run it, they'd need to have the required files, including:
C:\Program Files\MATLAB\R2022b\bin\my_excel_file.xls
I:\BIOM_Signal_processing\Hw5\ECGsignal_1.mat
I:\BIOM_Signal_processing\Hw5\ECGsignal_1.xls
另请参阅
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