MATLAB: Remove trend and detect peaks in a photoplethysmogram(PPG) signal

biosignalecgphotoplethysmogramppgrespirationsignalsignal processingtrend

There seems to be a sinusoidal like trend in my PPG signal. I believe this is due to respiration. I would like to detect the PPG peaks(maxima) in this signal after removing the trend or even better detect the peaks without removing the trends. Please help to provide a sample code as I am kind of new to signal processing for bio-signals.
clear all;
clc;
readData = csvread('p3_sit.csv',0,0);
ppg_head = readData(:,1).';
fs = 960; 
rec_time_minutes = (length(ppg_head)-1)/60000 %recording time calculation
wn=10/(fs/2); %lowpass 10Hz for ppg
[b,a] = butter(8,wn); 
ppg_head_data = filter(b,a,ppg_head);
N1 = length(ppg_head_data);
t1 = [0:N1-1]/fs;
%{comment here %}
figure(1)
plot(t1,ppg_head_data);
xlabel('second');ylabel('Volts');title('PPG Head Signal')

Best Answer

If you want to eliminate the baseline wander and offset, change to a highpass filter with a different cutoff frequency:
wn=5/(fs/2); %lowpass 10Hz for ppg
[b,a] = butter(8,wn,'high'); 
ppg_head_data = filter(b,a,ppg_head);
There are much better ways to design and implement filters. Here is one:
t = linspace(0, numel(ppg_head), numel(ppg_head))/fs;
Fs = 960;                                                   % Sampling Frequency (Hz)
Fn = Fs/2;                                                  % Nyquist Frequency (Hz)
Wp = 24.5/Fn;                                               % Stopband Frequency (Normalised)
Ws = 25.0/Fn;                                               % Passband Frequency (Normalised)
Rp =   1;                                                   % Passband Ripple (dB)
Rs =  50;                                                   % Stopband Ripple (dB)
[n,Ws] = cheb2ord(Wp,Ws,Rp,Rs);                             % Filter Order
[z,p,k] = cheby2(n,Rs,Ws,'high');                           % Filter Design, Sepcify Bandstop
[sos,g] = zp2sos(z,p,k);                                    % Convert To Second-Order-Section For Stability
figure(2)
freqz(sos, 2^16, Fs)                                        % Filter Bode Plot
ppg_head_data = filtfilt(sos, g, ppg_head);                 % Filter Signal
Use the Signal Processing Toolbox findpeaks (link) function on your filtered data to locate the peaks:
[pks,locs] = findpeaks(ppg_head_data, 'MinPeakHeight',2E+5);
You can use the ‘locs’ index vector to refer to the peaks and times in the original unfiltered signal.
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