Unable to find file , load file to xl file .

[Stephen23]

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.

Answer 1

[Voss]

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.

https://www.mathworks.com/help/matlab/ref/readmatrix.html

https://www.mathworks.com/help/matlab/ref/readtable.html

https://www.mathworks.com/help/matlab/ref/readcell.html

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).

[Md. Mohidul Islam]

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.

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;

[Md. Mohidul Islam]

The load file

[Walter Roberson]

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]

@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