Datasets:
| "keyword","repo_name","file_path","file_extension","file_size","line_count","content","language" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","main.m",".m","3040","58","% ========================================================================= | |
| % Below functions are the implementation for the comparison methods in | |
| % paper: | |
| % Han, Dong, Syed K. Bashar, Jesús Lázaro, Fahimeh Mohagheghian, | |
| % Andrew Peitzsch, Nishat Nishita, Eric Ding, Emily L. Dickson, | |
| % Danielle DiMezza, Jessica Scott, Cody Whitcomb, Timothy P. Fitzgibbons, | |
| % David D. McManus, and Ki H. Chon. 2022. | |
| % ""A Real-Time PPG Peak Detection Method for Accurate Determination of | |
| % Heart Rate during Sinus Rhythm and Cardiac Arrhythmia"" | |
| % Biosensors 12, no. 2: 82. https://doi.org/10.3390/bios12020082 | |
| % | |
| % Please cite our paper if you used our implementation code. Thank you. | |
| % Author: Dong Han (dong.han@uconn.edu), 01/31/2022. | |
| % ========================================================================= | |
| % ------------------------------------------------------------------------- | |
| % Input: | |
| % PPG_raw_buffer: should be 30-sec segment. | |
| % fs_PPG_raw: the sampling frequency of the PPG_raw_buffer. | |
| % ------------------------------------------------------------------------- | |
| %% Preparation of PPG signal: | |
| addpath('.\func') | |
| [PPG_buffer,fs_PPG] = my_func_prep_PPG_buffer(PPG_raw_buffer,fs_PPG_raw); | |
| %% Method 1: implemented method 1-a | |
| V_max_flag = true; % true == upper peak detection. | |
| addpath('.\method_01_and_02'); | |
| output_upper_Shin_2009 = my_peak_compare_Shin_2009(PPG_buffer,fs_PPG,V_max_flag); % Implementation of Shin 2009 paper. | |
| %% Method 2: implemented method 1-b | |
| V_max_flag = false; % false == lower peak detection. | |
| output_lower_Shin_2009 = my_peak_compare_Shin_2009(PPG_buffer,fs_PPG,V_max_flag); % Implementation of Shin 2009 paper. | |
| %% Method 3 & 4: implemented method 2, it has two output peaks in ""output_Elgendi_1_2013"" | |
| delta = 0.5; % it was 0.1 as mentioned in the paper. But I think 0.5 works better (0.5 is in the billauer's website). | |
| addpath('.\method_03_and_04'); | |
| [output_Elgendi_1_2013] = my_Elgendi_2013_method_I_peakdet(PPG_buffer, delta, fs_PPG); | |
| %% Method 5: first derivative and adaptive thresholding method in Li et al. [4] and Elgendi's paper [3] | |
| abpsig = resample(PPG_buffer,fs_abpsig,fs_PPG_buffer); % upsampling it to 125 Hz. | |
| addpath('.\method_05'); | |
| [output_Elgendi_2_2013] = my_func_ppg_peakdet_method_05_Elgendi_2013_method_II(abpsig,fs_abpsig); | |
| %% Method 6: implemented method 4 | |
| fs_abp = 250; % Hz. | |
| abp = resample(PPG_buffer,fs_abp,fs_PPG); % upsampling it to 125 Hz. | |
| addpath('.\method_06'); | |
| [output_Elgendi_3_2013] = my_Elgendi_2013_method_III_peakdet(abp,fs_abp); | |
| %% Method 7: event-related moving averages with dynamic threshold method in Elgendi et al.'s paper [3] | |
| addpath('.\method_07'); | |
| [output_Elgendi_4_2013] = my_func_ppg_peakdet_method_07_Elgendi_2013_method_IV(-PPG_raw_buffer,fs_PPG_raw); | |
| %% Method 8 & 9: peak detection on Stationary Wavelet Transform of PPG signal | |
| fs_swt = 125; % Hz. | |
| PPG_swt = resample(PPG_buffer,fs_swt,fs_PPG); % upsampling it to 125 Hz. | |
| addpath('.\method_08_and_09'); | |
| [output_Vadrevu_1_2019,output_Vadrevu_2_2019] = my_Vadrevu_2019_peakdet(PPG_swt,fs_swt);","MATLAB" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","method_07/my_func_ppg_peakdet_method_07_Elgendi_2013_method_IV.m",".m","6505","156","function output_Elgendi_4_2013 = my_func_ppg_peakdet_method_07_Elgendi_2013_method_IV(raw_PPG,fs_PPG) | |
| % ========================================================================= | |
| % This is my implementation of the method IV in this paper: | |
| % Elgendi, Mohamed, et al. | |
| % ""Systolic peak detection in acceleration photoplethysmograms measured from | |
| % emergency responders in tropical conditions."" PLoS One 8.10 (2013): e76585. | |
| % | |
| % Implemented by Dong Han on 03/02/2020. | |
| % | |
| % Please cite our paper if you used this code: | |
| % Han, Dong, Syed K. Bashar, Jesús Lázaro, Fahimeh Mohagheghian, | |
| % Andrew Peitzsch, Nishat Nishita, Eric Ding, Emily L. Dickson, | |
| % Danielle DiMezza, Jessica Scott, Cody Whitcomb, Timothy P. Fitzgibbons, | |
| % David D. McManus, and Ki H. Chon. 2022. | |
| % ""A Real-Time PPG Peak Detection Method for Accurate Determination of | |
| % Heart Rate during Sinus Rhythm and Cardiac Arrhythmia"" | |
| % Biosensors 12, no. 2: 82. https://doi.org/10.3390/bios12020082 | |
| % | |
| % Please cite our paper if you used our code. Thank you. | |
| % ========================================================================= | |
| %% pre-processing - bandpass filtering | |
| [b, a] = butter(2,[0.5 8]/(fs_PPG/2)); % 2nd order bandpass filter 0.5-8Hz; | |
| filtered_PPG = filtfilt(b, a, raw_PPG); % zero-phase filter. | |
| filtered_PPG = filtered_PPG ./ std(filtered_PPG); % normalizing data is very important for my peak detection. | |
| filtered_PPG = filtered_PPG - mean(filtered_PPG); | |
| debugging_plot_flag = false; % only for plotting debugging figures. | |
| % clip the signal by keeping the signal above zero. | |
| % I do not want to do this, so i will move all signal above zero. | |
| S_n = filtered_PPG; | |
| % ---- Not following the paper to clip signal but move all signal above zero: | |
| % if min(S_n) < 0 | |
| % Z_n = S_n - min(S_n); % elevate signal above zero. | |
| % else | |
| % % the minimum of S_n is still above zero, so do nothing. | |
| % Z_n = S_n; | |
| % end | |
| % ---- Following the paper: only keep the positive value: | |
| Z_n = S_n; | |
| Z_n(Z_n < 0) = 0; | |
| %% pre-processing - squaring | |
| y_n = (Z_n).^2; % element-wise power. | |
| %% feature extraction - generating potential blocks using two moving averages | |
| W_1 = round(0.111 * fs_PPG); % mentioned as the paper by brute-force search. | |
| % first moving average: | |
| % MA_peak = y_n; % for the beginning and ending signal, use the original signal. | |
| % for nn = 1+round(W_1/2):length(raw_PPG)-round(W_1/2) | |
| % temp_range = (nn-round(W_1/2)):(nn+round(W_1/2)); | |
| % MA_peak(nn) = sum(y_n(temp_range))/W_1; | |
| % end | |
| MA_peak = movmean(y_n,W_1); | |
| % second moving average: | |
| W_2 = round(0.667 * fs_PPG); | |
| % MA_beat = y_n; | |
| % for nn = 1+round(W_2/2):length(raw_PPG)-round(W_2/2) | |
| % temp_range = (nn-round(W_2/2)):(nn+round(W_2/2)); | |
| % MA_beat(nn) = sum(y_n(temp_range))/W_2; | |
| % end | |
| MA_beat = movmean(y_n,W_2); | |
| %% classification - thresholding | |
| beta = 0.02; % from the paper, by brute force search. | |
| z_bar = mean(y_n); | |
| alpha = beta * z_bar; % offset level. | |
| THR_1 = MA_beat + alpha; | |
| Blocks_Of_Interest = zeros(size(MA_peak)); % I initial it as zero. | |
| for nn = 1:length(MA_peak) | |
| if MA_peak(nn) > THR_1(nn) % I think it is THR_1(nn). | |
| Blocks_Of_Interest(nn) = 0.1; | |
| else | |
| % since I inital block of interest as zero, so I do not need to | |
| % assign zero again. | |
| end | |
| end | |
| % searh for onset and offset of each block. | |
| count_blocks = 0; | |
| block_onset = NaN(size(MA_peak)); | |
| block_offset = NaN(size(MA_peak)); | |
| if any(Blocks_Of_Interest > 0) % there is a block exist. | |
| for nn = 1:length(MA_peak) | |
| if nn == 1 && Blocks_Of_Interest(nn) > 0 | |
| % the first point is a block; | |
| count_blocks = count_blocks + 1; % since the block start from zero, I have to add the counter first. | |
| block_onset(count_blocks,1) = nn; | |
| elseif nn == length(MA_peak) && Blocks_Of_Interest(nn) > 0 | |
| % end with a block: | |
| % no need to add count_blocks; | |
| block_offset(count_blocks,1) = nn; | |
| else | |
| if nn > 1 | |
| if Blocks_Of_Interest(nn-1) == 0 && Blocks_Of_Interest(nn) > 0 % a jump means a new block. | |
| count_blocks = count_blocks + 1; | |
| block_onset(count_blocks,1) = nn; | |
| elseif Blocks_Of_Interest(nn-1) > 0 && Blocks_Of_Interest(nn) == 0 % a drop means the end of previous block. | |
| block_offset(count_blocks,1) = nn; | |
| end | |
| end | |
| end | |
| end | |
| else | |
| % there is no block existed. Check why. | |
| % keyboard; | |
| HR_Elgendi_4_2013 = 0; % there is no peak location. | |
| S_peaks = 1; | |
| output_Elgendi_4_2013 = struct('filtered_PPG_Elgendi_4_2013',S_n,... | |
| 'PPG_peak_loc_Elgendi_4_2013',S_peaks,... | |
| 'HR_Elgendi_4_2013',HR_Elgendi_4_2013); | |
| return | |
| end | |
| block_onset(isnan(block_onset)) = []; % remove extra elements. | |
| block_offset(isnan(block_offset)) = []; % remove extra elements. | |
| if size(block_onset,1) ~= size(block_offset,1) | |
| % not same number of onset and offset, check here. | |
| keyboard; | |
| end | |
| if size(block_onset,1) ~= count_blocks | |
| keyboard; | |
| end | |
| S_peaks = NaN(count_blocks,1); | |
| THR_2 = W_1; | |
| for jj = 1:count_blocks | |
| block_idx = [block_onset(jj,1):block_offset(jj,1)]; | |
| [~,I] = max(y_n(block_idx)); | |
| S_peaks(jj,1) = block_onset(jj,1) + I - 1; | |
| end | |
| if debugging_plot_flag | |
| figure; | |
| plot(filtered_PPG);hold on; | |
| plot(S_peaks,y_n(S_peaks),'r.','markersize',10); | |
| plot(y_n); | |
| plot(MA_peak,'k:'); | |
| plot(MA_beat,'r--'); | |
| plot(THR_1,'g.-'); | |
| plot(Blocks_Of_Interest*max(y_n)*10,'color',[0.5,0.5,0.5]); % grey color. I want to make block more obvious. | |
| legend('filtered PPG','peaks', 'squared PPG with clip to zero', 'MA peak', 'MA beat','THR 1', 'Blocks of Interest'); | |
| end | |
| if isempty(S_peaks) | |
| HR_Elgendi_4_2013 = 0; % there is no peak location. | |
| S_peaks = 1; | |
| else | |
| HR_Elgendi_4_2013 = 60 * fs_PPG ./ diff(S_peaks); % calculate the HR. | |
| end | |
| output_Elgendi_4_2013 = struct('filtered_PPG_Elgendi_4_2013',S_n,... | |
| 'PPG_peak_loc_Elgendi_4_2013',S_peaks,... | |
| 'HR_Elgendi_4_2013',HR_Elgendi_4_2013); | |
| end","MATLAB" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","method_01_and_02/my_peak_compare_Shin_2009.m",".m","21523","388","function [output_Shin_2009] = my_peak_compare_Shin_2009(raw_PPG,fs_PPG,V_max_flag) | |
| % ========================================================================= | |
| % This function is the implementation of this paper: | |
| % Shin, Hang Sik, Chungkeun Lee, and Myoungho Lee. | |
| % ""Adaptive threshold method for the peak detection of | |
| % photoplethysmographic waveform."" | |
| % Computers in biology and medicine | |
| % 39.12 (2009): 1145-1152. | |
| % | |
| % Implemented by: Dong Han, on 02/10/2020. | |
| % | |
| % Please cite our paper if you used this code: | |
| % Han, Dong, Syed K. Bashar, Jesús Lázaro, Fahimeh Mohagheghian, | |
| % Andrew Peitzsch, Nishat Nishita, Eric Ding, Emily L. Dickson, | |
| % Danielle DiMezza, Jessica Scott, Cody Whitcomb, Timothy P. Fitzgibbons, | |
| % David D. McManus, and Ki H. Chon. 2022. | |
| % ""A Real-Time PPG Peak Detection Method for Accurate Determination of | |
| % Heart Rate during Sinus Rhythm and Cardiac Arrhythmia"" | |
| % Biosensors 12, no. 2: 82. https://doi.org/10.3390/bios12020082 | |
| % | |
| % Please cite our paper if you used our code. Thank you. | |
| % ========================================================================= | |
| debugging_plot_flag = false; % debugging plot. Can be false if don't want to plot anything. | |
| %% Section 2.4 PPG frequency analysis and filtering. | |
| % (1): high pass >= 0.5 Hz. | |
| [b, a] = butter(6,[0.5 20]/(fs_PPG/2)); % bandpass filter 0.5-10Hz, changed from 0.5-20 to 0.5-9 Hz at 11/21/2018 | |
| raw_PPG = filtfilt(b, a, raw_PPG); % -> AC component | |
| raw_PPG = raw_PPG ./ std(raw_PPG); % normalizing data is very important for my peak detection. | |
| raw_PPG = raw_PPG - mean(raw_PPG); | |
| %% Section 2.5 & 2.6 Peak detection algorithm & Adaptive threshold detection | |
| % (1): bandpass filtering, no moving average filter or wavelet | |
| % decomposition. | |
| filtered_PPG = raw_PPG; | |
| Fs = fs_PPG; | |
| % % ===== interpolation to 1kHz of PPG: ===== | |
| % x = 1:length(filtered_PPG); | |
| % v = filtered_PPG; | |
| % | |
| % upsample_Fs = 250; | |
| % xq = 1:Fs/upsample_Fs:length(filtered_PPG); | |
| % vq1 = interp1(x,v,xq); | |
| % | |
| % filtered_PPG = vq1; | |
| % Fs = upsample_Fs; % upsampled to 1000 Hz. | |
| % figure | |
| % plot(x,v,'o',xq,vq1,':.'); | |
| % xlim([0 max(xq)]); | |
| % title('(Default) Linear Interpolation'); | |
| % (2): V_max | |
| % slope_k: k-th slope amplitude; | |
| % s_r: slope changing rate (empirically: V_max = -0.6); | |
| % V_n_1: previous peak amplitude; | |
| % std_PPG: standard deviation of entire PPG signal; | |
| % Fs: sampling frequency. | |
| filtered_PPG = filtered_PPG(:); | |
| slope_k = NaN(size(filtered_PPG)); % should be a column vector. | |
| peak_loc = NaN(size(filtered_PPG)); % the array to store PPG peak index. | |
| pk_idx = 1; % the counter of peaks. | |
| %% Section 2.7: Peak Correction | |
| refractory_period = 0.6 * Fs; % sec * sampling frequency, initial refractory period is 0.6 sec. | |
| temp_win_left = round(0.15 * Fs); % sec * sampling frequency. This is the search region for local minima or maxima detection. chose 0.15 sec because 0.3 sec == 200 BPM. | |
| temp_win_right = round(0.15 * Fs); | |
| if V_max_flag % doing upper peak detection. | |
| s_r = -0.6; | |
| else | |
| s_r = 0.6;%0.6; % not positive because my signal is zero mean. | |
| % I need to make all bottom signal positive, so I am moving them up. | |
| % move_filter_amp = min(filtered_PPG) * (-1); | |
| % filtered_PPG = filtered_PPG + move_filter_amp + std(raw_PPG); % move the lowest value more than zero. | |
| end | |
| slope_meet_PPG_flag = false; % mark if the slope meet PPG. | |
| slope_lower_PPG_flag = false; % mark if slope is lower than PPG, once PPG amp is lower than slope, mark it back. | |
| prev_slope = NaN; % First, I want to test not decreasing with PPG amplitude. | |
| if debugging_plot_flag % debugging plot | |
| figure; | |
| plot(filtered_PPG); | |
| hold on; | |
| end | |
| for kk = 1:length(filtered_PPG) | |
| % this is for debugging: | |
| if kk == 2 | |
| my_stop = 1; | |
| end | |
| if kk == 1 % initial the slope value | |
| if V_max_flag | |
| slope_k(1,1) = 0.2 * max(filtered_PPG); | |
| std_PPG = std(filtered_PPG); | |
| else | |
| slope_k(1,1) = 0.2 * min(filtered_PPG); % since my signal is zero mean, I start from the negative amp. % I added what I moved. | |
| std_PPG = -std(filtered_PPG); | |
| end | |
| % std_PPG = std(filtered_PPG); | |
| V_n_1 = slope_k(1,1); | |
| else | |
| if slope_meet_PPG_flag % slope has met PPG before. | |
| slope_k(kk,1) = filtered_PPG(kk,1); | |
| if V_max_flag % upper peak detection. | |
| if kk < 2 % in the second point of signal | |
| turn_point_flag = (slope_k(kk,1) < slope_k(kk-1,1)); % we met local maximum. | |
| else | |
| turn_point_flag = (slope_k(kk,1) < slope_k(kk-1,1)) & (slope_k(kk - 1,1) > slope_k(kk-2,1)); % we met local maximum. | |
| end | |
| else | |
| if kk < 2 % in the second point of signal | |
| turn_point_flag = (slope_k(kk,1) > slope_k(kk-1,1)); % we met local minimum. | |
| else | |
| turn_point_flag = (slope_k(kk,1) > slope_k(kk-1,1)) & (slope_k(kk - 1,1) < slope_k(kk-2,1)); % we met local minimum. | |
| end | |
| end | |
| if turn_point_flag % there is a turning point. | |
| if pk_idx > 1 % not the first peak | |
| % check local maxima or minima: | |
| if (kk - temp_win_left) < 1 | |
| temp_left = 1; | |
| else | |
| temp_left = kk - temp_win_left; | |
| end | |
| if (kk + temp_win_right) > length(filtered_PPG) | |
| temp_right = length(filtered_PPG); | |
| else | |
| temp_right = kk + temp_win_right; | |
| end | |
| temp_win = temp_left:temp_right; | |
| local_m_check = filtered_PPG(temp_win); | |
| if V_max_flag | |
| temp_m_idx = find(local_m_check > slope_k(kk - 1,1)); % check if there is another maximum than detected, remember use k-1. | |
| else | |
| temp_m_idx = find(local_m_check < slope_k(kk - 1,1)); % check if there is another minimum than detected | |
| end | |
| if isempty(temp_m_idx) % there is no more max or min than this peak | |
| if (kk - peak_loc(pk_idx-1,1) > refractory_period) % it is not the first peak, and the second peak is outside refractory period. It should be kk, because I have not assign the peak to the array. | |
| peak_loc(pk_idx,1) = kk-1; | |
| V_n_1 = filtered_PPG(peak_loc(pk_idx-1,1),1);% previous peak amplitude %slope_k(kk-1,1); | |
| % update refractory period: | |
| refractory_period = 0.6 * (kk - peak_loc(pk_idx-1,1)); % current index minus peak location. update the refractory peroid before updating the peak counting. | |
| pk_idx = pk_idx + 1; | |
| % reset slope meet flag: | |
| slope_meet_PPG_flag = false; | |
| slope_k(kk,1) = slope_k(kk - 1,1) + s_r * ((V_n_1 + std_PPG) / Fs); | |
| % ---- for checking lower slope ------- | |
| temp_slope_check = s_r * ((V_n_1 + std_PPG) / Fs); | |
| if V_max_flag | |
| if temp_slope_check > 0 % upper peaks should be decreasing with negative slope. | |
| temp_slope_check = -s_r * ((V_n_1 + std_PPG) / Fs);%-temp_slope_check; | |
| slope_k(kk,1) = slope_k(kk - 1,1) + temp_slope_check; | |
| end | |
| else | |
| if temp_slope_check < 0 % upper peaks should be decreasing with negative slope. | |
| temp_slope_check = -s_r * ((V_n_1 + std_PPG) / Fs);%-temp_slope_check; | |
| slope_k(kk,1) = slope_k(kk - 1,1) + temp_slope_check; | |
| end | |
| end | |
| % ------------------------------------------- | |
| if V_max_flag | |
| temp_slope_below_PPG_flag = slope_k(kk,1) < filtered_PPG(kk,1); % upper peak detection, so slope below signal. | |
| else | |
| temp_slope_below_PPG_flag = slope_k(kk,1) > filtered_PPG(kk,1); % lower peak detection, so slope above signal. | |
| end | |
| if temp_slope_below_PPG_flag % if slope is below PPG signal, we will reset slope value to PPG amplitude. | |
| slope_lower_PPG_flag = true; % slope is lower than PPG signal. | |
| prev_slope = slope_k(kk,1); % store the slope value now. | |
| slope_k(kk,1) = filtered_PPG(kk,1); | |
| end | |
| if debugging_plot_flag % debugging plot | |
| plot(kk,slope_k(kk,1),'r.'); | |
| end | |
| else | |
| if (kk - peak_loc(pk_idx-1,1) <= refractory_period) % it is because of the refractory period that cause the no peak. It should be kk, because I have not assign the peak to the array. | |
| slope_k(kk,1) = filtered_PPG(kk,1);% from the fig.3(c) in the paper, I see they are using the signal amplitude, not slope. | |
| % no need to reset slope meet flag, waiting for | |
| % next turning point. | |
| if debugging_plot_flag % debugging plot | |
| plot(kk,slope_k(kk,1),'r.'); | |
| end | |
| end | |
| end | |
| else % there are more peaks higher then current kk peak. | |
| if debugging_plot_flag % debugging plot | |
| plot(kk,slope_k(kk,1),'r.'); | |
| end | |
| end | |
| else % the first peak, no need to check refractory period. | |
| % check local maxima or minima: | |
| if (kk - temp_win_left) < 1 | |
| temp_left = 1; | |
| else | |
| temp_left = kk - temp_win_left; | |
| end | |
| if (kk + temp_win_right) > length(filtered_PPG) | |
| temp_right = length(filtered_PPG); | |
| else | |
| temp_right = kk + temp_win_right; | |
| end | |
| temp_win = temp_left:temp_right; | |
| local_m_check = filtered_PPG(temp_win); | |
| if V_max_flag | |
| temp_m_idx = find(local_m_check > slope_k(kk-1,1)); % check if there is another maximum than detected, always detect previous peak. | |
| else | |
| temp_m_idx = find(local_m_check < slope_k(kk-1,1)); % check if there is another minimum than detected | |
| end | |
| if isempty(temp_m_idx) | |
| peak_loc(pk_idx,1) = kk-1; | |
| if pk_idx > 1 | |
| V_n_1 = filtered_PPG(peak_loc(pk_idx-1,1),1); | |
| else | |
| V_n_1 = slope_k(kk-1,1);% previous peak amplitude %slope_k(kk-1,1); | |
| end | |
| pk_idx = pk_idx + 1; | |
| % reset slope meet flag: | |
| slope_meet_PPG_flag = false; | |
| slope_k(kk,1) = slope_k(kk - 1,1) + s_r * ((V_n_1 + std_PPG) / Fs); | |
| % ---- for checking lower slope ------- | |
| temp_slope_check = s_r * ((V_n_1 + std_PPG) / Fs); | |
| if V_max_flag | |
| if temp_slope_check > 0 % upper peaks should be decreasing with negative slope. | |
| temp_slope_check = -s_r * ((V_n_1 + std_PPG) / Fs);%-temp_slope_check; | |
| slope_k(kk,1) = slope_k(kk - 1,1) + temp_slope_check; | |
| end | |
| else | |
| if temp_slope_check < 0 % upper peaks should be decreasing with negative slope. | |
| temp_slope_check = -s_r * ((V_n_1 + std_PPG) / Fs);%-temp_slope_check; | |
| slope_k(kk,1) = slope_k(kk - 1,1) + temp_slope_check; | |
| end | |
| end | |
| % ------------------------------------------- | |
| if V_max_flag | |
| temp_slope_below_PPG_flag = slope_k(kk,1) < filtered_PPG(kk,1); % upper peak detection, so slope below signal. | |
| else | |
| temp_slope_below_PPG_flag = slope_k(kk,1) > filtered_PPG(kk,1); % lower peak detection, so slope above signal. | |
| end | |
| if temp_slope_below_PPG_flag % if slope is below PPG signal, we will reset slope value to PPG amplitude. | |
| slope_k(kk,1) = filtered_PPG(kk,1); | |
| end | |
| if debugging_plot_flag % debugging plot | |
| plot(kk,slope_k(kk,1),'r.'); | |
| end | |
| else % there are more peaks higher then current kk peak. | |
| if debugging_plot_flag % debugging plot | |
| plot(kk,slope_k(kk,1),'r.'); | |
| end | |
| end | |
| % no need to calculate refractory period, because there is only one peak, at least two peaks can give this correctly: | |
| end | |
| else | |
| % turning point did not meet, so keep decreasing or | |
| % increasing the slope. | |
| % slope_k(kk,1) = slope_k(kk - 1,1) + s_r * ((V_n_1 + std_PPG) / Fs); | |
| if debugging_plot_flag % debugging plot | |
| plot(kk,slope_k(kk,1),'r.'); | |
| end | |
| end | |
| else % slope has not met PPG before. Keep decresing or increasing according to 'V_max_flag'. | |
| % if slope_lower_PPG_flag % if there is a slope lower than PPG before: | |
| % slope_k(kk,1) = prev_slope; | |
| % else | |
| slope_k(kk,1) = slope_k(kk - 1,1) + s_r * ((V_n_1 + std_PPG) / Fs); | |
| % ---- for checking lower slope ------- | |
| temp_slope_check = s_r * ((V_n_1 + std_PPG) / Fs); | |
| if V_max_flag | |
| if temp_slope_check > 0 % upper peaks should be decreasing with negative slope. | |
| temp_slope_check = -s_r * ((V_n_1 + std_PPG) / Fs);%-temp_slope_check; | |
| slope_k(kk,1) = slope_k(kk - 1,1) + temp_slope_check; | |
| end | |
| else | |
| if temp_slope_check < 0 % upper peaks should be decreasing with negative slope. | |
| temp_slope_check = -s_r * ((V_n_1 + std_PPG) / Fs);%-temp_slope_check; | |
| slope_k(kk,1) = slope_k(kk - 1,1) + temp_slope_check; | |
| end | |
| end | |
| % ------------------------------------------- | |
| % end | |
| % if slope_k(kk,1) < filtered_PPG(kk,1) % if slope is below PPG signal, we will reset slope value to PPG amplitude. | |
| % slope_lower_PPG_flag = true; % slope is lower than PPG signal. | |
| % prev_slope = slope_k(kk,1); % store the slope value now. | |
| % slope_k(kk,1) = filtered_PPG(kk,1); | |
| % elseif slope_k(kk,1) > filtered_PPG(kk,1) % slope is higher. | |
| % slope_lower_PPG_flag = false; | |
| % prev_slope = NaN; % reset the prev value. | |
| % end | |
| % if slope_lower_PPG_flag ~= 1 % if slope was not lower than PPG. | |
| % % -------------- Check if two lines will meet ----------------- | |
| % PPG_x1 = kk - 1; | |
| % PPG_x2 = kk; | |
| % PPG_y1 = filtered_PPG(kk-1,1); | |
| % PPG_y2 = filtered_PPG(kk,1); | |
| % slope = s_r; | |
| % slope_y2 = slope_k(kk,1); | |
| % slope_y1 = slope_k(kk-1,1); | |
| % [meet_x] = my_slope_meet_PPG(PPG_x1,PPG_x2,PPG_y1,PPG_y2,slope,slope_y2,slope_y1); | |
| % | |
| % slope_meet_PPG_flag = (ceil(meet_x) == kk);%(slope_k(kk,1) - filtered_PPG(kk,1)) < 0.1; % 0.3 is a testing value. %slope_k(kk,1) == filtered_PPG(kk,1) % slope meets the PPG signal. | |
| % end | |
| if V_max_flag | |
| slope_meet_PPG_flag = ((slope_k(kk,1) < filtered_PPG(kk,1)) & slope_k(kk - 1,1) > filtered_PPG(kk - 1,1)); | |
| else | |
| slope_meet_PPG_flag = ((slope_k(kk,1) > filtered_PPG(kk,1)) & slope_k(kk - 1,1) < filtered_PPG(kk - 1,1)); % lower peak use inverse amplitude. | |
| end | |
| % ------------------------------------------------------------- | |
| % I found I cannot use equal, because the PPG sampling | |
| % frequency is not so high. | |
| if slope_meet_PPG_flag | |
| slope_k(kk,1) = filtered_PPG(kk,1); % starts from the next index, slope == PPG amplitude. | |
| else | |
| % don't need to do anything. | |
| if slope_lower_PPG_flag ~= 1 % there was no slope lower than PPG before. | |
| if V_max_flag | |
| slope_lower_PPG_flag = ((slope_k(kk,1) < filtered_PPG(kk,1)) & slope_k(kk - 1,1) == filtered_PPG(kk - 1,1)); % beginning part has same amplitude, but the ending part slope is lower. | |
| else | |
| slope_lower_PPG_flag = ((slope_k(kk,1) > filtered_PPG(kk,1)) & slope_k(kk - 1,1) == filtered_PPG(kk - 1,1)); % lower peak use inverse amplitude. | |
| end | |
| if slope_lower_PPG_flag | |
| prev_slope = slope_k(kk,1); % store the slope value now. | |
| slope_k(kk,1) = filtered_PPG(kk,1); % starts from the next index, slope == PPG amplitude. | |
| end | |
| else % there was slope lower than PPG before. | |
| if V_max_flag | |
| temp_PPG_below_slope_flag = filtered_PPG(kk,1) < prev_slope; % upper peak detection, so PPG below slope. | |
| else | |
| temp_PPG_below_slope_flag = filtered_PPG(kk,1) > prev_slope; % lower peak detection, so PPG above slope. | |
| end | |
| if temp_PPG_below_slope_flag % PPG is lower than prev slope. | |
| slope_k(kk,1) = prev_slope; % stop tracking PPG amp. | |
| slope_lower_PPG_flag = false; % reset the lower PPG flag. | |
| prev_slope = NaN; | |
| else | |
| slope_k(kk,1) = filtered_PPG(kk,1); % keep tracking PPG amp. | |
| end | |
| end | |
| end | |
| if debugging_plot_flag % debugging plot | |
| plot(kk,slope_k(kk,1),'r.'); | |
| end | |
| end | |
| end | |
| end | |
| % ================== IMPORTANT: clean up NaN value ======================== | |
| peak_loc(isnan(peak_loc)) = []; % remove empty peak loc. | |
| if V_max_flag % doing upper peak detection. | |
| else | |
| % moving signal back. | |
| % filtered_PPG = filtered_PPG - move_filter_amp - std(raw_PPG); % move the lowest value more than zero. | |
| % slope_k = slope_k - move_filter_amp - std(raw_PPG); % move the slope as well. | |
| end | |
| if debugging_plot_flag % debugging plot | |
| plot(peak_loc,filtered_PPG(peak_loc),'ko'); | |
| end | |
| if isempty(peak_loc) | |
| HR_Shin_2009 = 0; % there is no peak location. | |
| peak_loc = 1; | |
| else | |
| HR_Shin_2009 = 60 * Fs ./ diff(peak_loc); % calculate the HR. | |
| end | |
| output_Shin_2009 = struct('PPG_peak_loc_Shin_2009',peak_loc,... | |
| 'slope_Shin_2009',slope_k,... | |
| 'filtered_PPG_Shin_2009',filtered_PPG,... | |
| 'HR_Shin_2009',HR_Shin_2009); | |
| end","MATLAB" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","method_05/my_func_ppg_peakdet_method_05_Elgendi_2013_method_II.m",".m","11993","412","function [output_Elgendi_2_2013] = my_func_ppg_peakdet_method_05_Elgendi_2013_method_II(raw_PPG,fs_PPG) | |
| % ------------------------------------------------------------------------- | |
| % This peak detection function was mentioned in this paper: | |
| % Elgendi, Mohamed, et al. | |
| % ""Systolic peak detection in acceleration photoplethysmograms measured from | |
| % emergency responders in tropical conditions."" PLoS One 8.10 (2013): e76585. | |
| % | |
| [onsetp,peakp,dicron,abpsig] = delineator(raw_PPG,fs_PPG); | |
| % ------------------------------------------------------------------------- | |
| if isempty(peakp) % there is no peak detected: | |
| HR_Elgendi_2_2013 = 0; % there is no peak location. | |
| peakp = 1; | |
| else | |
| HR_Elgendi_2_2013 = 60 * fs_PPG ./ diff(peakp); % calculate the HR. | |
| end | |
| output_Elgendi_2_2013 = struct('PPG_peak_loc_Elgendi_2_2013',peakp,... | |
| 'HR_Elgendi_2_2013',HR_Elgendi_2_2013,... | |
| 'filtered_PPG_Elgendi_2_2013',abpsig); | |
| end | |
| function [onsetp,peakp,dicron,abpsig] = delineator(abpsig,abpfreq) | |
| % Below was copied from Mathwords File Exchange ""Pulse Waveform Delineator"": | |
| % https://www.mathworks.com/matlabcentral/fileexchange/29484-pulse-waveform-delineator | |
| % This program is intended to delineate the fiducial points of pulse waveforms | |
| % Inputs: | |
| % abpsig: input as original pulse wave signals; | |
| % abpfreq: input as the sampling frequency; | |
| % Outputs: | |
| % onsetp: output fiducial points as the beginning of each beat; | |
| % peakp: output fiducial points as systolic peaks; | |
| % dicron: output fiducial points as dicrotic notches; | |
| % Its delineation is based on the self-adaptation in pulse waveforms, but | |
| % not in the differentials. | |
| % Reference: | |
| % BN Li, MC Dong & MI Vai (2010) | |
| % On an automatic delineator for arterial blood pressure waveforms | |
| % Biomedical Signal Processing and Control 5(1) 76-81. | |
| % LI Bing Nan @ University of Macau, Feb 2007 | |
| % Revision 2.0.5, Apr 2009 | |
| %Initialization | |
| peakIndex=0; | |
| onsetIndex=0; | |
| dicroIndex=0; | |
| stepWin=2*abpfreq; | |
| closeWin=floor(0.1*abpfreq); %invalide for pulse beat > 200BPM | |
| sigLen=length(abpsig); | |
| peakp=[]; | |
| onsetp=[]; | |
| dicron=[]; | |
| %lowpass filter at first | |
| coh=25; %cutoff frequency is 25Hz | |
| coh=coh*2/abpfreq; | |
| od=3; %3rd order bessel filter | |
| [B,A]=besself(od,coh); | |
| abpsig=filter(B,A,abpsig); | |
| abpsig=10*abpsig; | |
| abpsig=smooth(abpsig); | |
| %Compute differentials | |
| ttp=diff(abpsig); | |
| diff1(2:sigLen)=ttp; | |
| diff1(1)=diff1(2); | |
| diff1=100*diff1; | |
| clear ttp; | |
| diff1=smooth(diff1); | |
| if sigLen>12*abpfreq | |
| tk=10; | |
| elseif sigLen>7*abpfreq | |
| tk=5; | |
| elseif sigLen>4*abpfreq | |
| tk=2; | |
| else | |
| tk=1; | |
| end | |
| %Seek avaerage threshold in original signal | |
| if tk>1 %self-learning threshold with interval sampling | |
| tatom=floor(sigLen/(tk+2)); | |
| for ji=1:tk %search the slopes of abp waveforms | |
| sigIndex=ji*tatom; | |
| tempIndex=sigIndex+abpfreq; | |
| [tempMin,jk,tempMax,jl]=seeklocales(abpsig,sigIndex,tempIndex); | |
| tempTH(ji)=tempMax-tempMin; | |
| end | |
| abpMaxTH=mean(tempTH); | |
| else | |
| [tempMin,jk,tempMax,jl]=seeklocales(abpsig,closeWin,sigLen); | |
| abpMaxTH=tempMax-tempMin; | |
| end | |
| clear j*; | |
| clear t*; | |
| abpMaxLT=0.4*abpMaxTH; | |
| %Seek pulse beats by MinMax method | |
| % diffIndex=1; | |
| diffIndex=closeWin; %Avoid filter distortion | |
| while diffIndex<sigLen | |
| tempMin=abpsig(diffIndex); %Initialization | |
| tempMax=abpsig(diffIndex); | |
| tempIndex=diffIndex; | |
| tpeakp=diffIndex; %Avoid initial error | |
| tonsetp=diffIndex; %Avoid initial error | |
| while tempIndex<sigLen | |
| %If no pulses within 2s, then adjust threshold and retry | |
| if (tempIndex-diffIndex)>stepWin | |
| % tempIndex=diffIndex-closeWin; | |
| tempIndex=diffIndex; | |
| abpMaxTH=0.6*abpMaxTH; | |
| if abpMaxTH<=abpMaxLT | |
| abpMaxTH=2.5*abpMaxLT; | |
| end | |
| break; | |
| end | |
| if (diff1(tempIndex-1)*diff1(tempIndex+1))<=0 %Candidate fiducial points | |
| if (tempIndex+5)<=sigLen | |
| jk=tempIndex+5; | |
| else | |
| jk=sigLen; | |
| end | |
| if (tempIndex-5)>=1 | |
| jj=tempIndex-5; | |
| else | |
| jj=1; | |
| end | |
| %Artifacts of oversaturated or signal loss? | |
| if (jk-tempIndex)>=5 | |
| for ttk=tempIndex:jk | |
| if diff1(ttk)~=0 | |
| break; | |
| end | |
| end | |
| if ttk==jk | |
| break; %Confirm artifacts | |
| end | |
| end | |
| if diff1(jj)<0 %Candidate onset | |
| if diff1(jk)>0 | |
| [tempMini,tmin,ta,tb]=seeklocales(abpsig,jj,jk); | |
| if abs(tmin-tempIndex)<=2 | |
| tempMin=tempMini; | |
| tonsetp=tmin; | |
| end | |
| end | |
| elseif diff1(jj)>0 %Candidate peak | |
| if diff1(jk)<0 | |
| [tc,td,tempMaxi,tmax]=seeklocales(abpsig,jj,jk); | |
| if abs(tmax-tempIndex)<=2 | |
| tempMax=tempMaxi; | |
| tpeakp=tmax; | |
| end | |
| end | |
| end | |
| if ((tempMax-tempMin)>0.4*abpMaxTH) %evaluation | |
| if ((tempMax-tempMin)<2*abpMaxTH) | |
| if tpeakp>tonsetp | |
| %If more zero-crossing points, further refine! | |
| ttempMin=abpsig(tonsetp); | |
| ttonsetp=tonsetp; | |
| for ttk=tpeakp:-1:(tonsetp+1) | |
| if abpsig(ttk)<ttempMin | |
| ttempMin=abpsig(ttk); | |
| ttonsetp=ttk; | |
| end | |
| end | |
| tempMin=ttempMin; | |
| tonsetp=ttonsetp; | |
| if peakIndex>0 | |
| %If pulse period less than eyeclose, then artifact | |
| if (tonsetp-peakp(peakIndex))<(3*closeWin) | |
| %too many fiducial points, then reset | |
| tempIndex=diffIndex; | |
| abpMaxTH=2.5*abpMaxLT; | |
| break; | |
| end | |
| %If pulse period bigger than 2s, then artifact | |
| if (tpeakp-peakp(peakIndex))>stepWin | |
| peakIndex=peakIndex-1; | |
| onsetIndex=onsetIndex-1; | |
| if dicroIndex>0 | |
| dicroIndex=dicroIndex-1; | |
| end | |
| end | |
| if peakIndex>0 | |
| %new pulse beat | |
| peakIndex=peakIndex+1; | |
| peakp(peakIndex)=tpeakp; | |
| onsetIndex=onsetIndex+1; | |
| onsetp(onsetIndex)=tonsetp; | |
| tf=onsetp(peakIndex)-onsetp(peakIndex-1); | |
| to=floor(abpfreq./20); %50ms | |
| tff=floor(0.1*tf); | |
| if tff<to | |
| to=tff; | |
| end | |
| to=peakp(peakIndex-1)+to; | |
| te=floor(abpfreq./2); %500ms | |
| tff=floor(0.5*tf); | |
| if tff<te | |
| te=tff; | |
| end | |
| te=peakp(peakIndex-1)+te; | |
| % Dong added on 05/07/2020: | |
| % For MIMIC III PACPVC 3_2, ii = 25. | |
| if te > length(diff1) | |
| te = length(diff1); | |
| end | |
| tff=seekdicrotic(diff1(to:te)); | |
| if tff==0 | |
| tff=te-peakp(peakIndex-1); | |
| tff=floor(tff/3); | |
| end | |
| dicroIndex=dicroIndex+1; | |
| dicron(dicroIndex)=to+tff; | |
| tempIndex=tempIndex+closeWin; | |
| break; | |
| end | |
| end | |
| if peakIndex==0 %new pulse beat | |
| peakIndex=peakIndex+1; | |
| peakp(peakIndex)=tpeakp; | |
| onsetIndex=onsetIndex+1; | |
| onsetp(onsetIndex)=tonsetp; | |
| tempIndex=tempIndex+closeWin; | |
| break; | |
| end | |
| end | |
| end | |
| end | |
| end | |
| tempIndex=tempIndex+1; %step forward | |
| end | |
| % diffIndex=tempIndex+closeWin; %for a new beat | |
| diffIndex=tempIndex+1; | |
| end | |
| if isempty(peakp),return;end | |
| %Compensate the offsets of lowpass filter | |
| sigLen=length(peakp); | |
| for diffIndex=1:sigLen %avoid edge effect | |
| tempp(diffIndex)=peakp(diffIndex)-od; | |
| end | |
| ttk=tempp(1); | |
| if ttk<=0 | |
| tempp(1)=1; | |
| end | |
| clear peakp; | |
| peakp=tempp; | |
| clear tempp; | |
| sigLen=length(onsetp); | |
| for diffIndex=1:sigLen | |
| tempp(diffIndex)=onsetp(diffIndex)-od; | |
| end | |
| ttk=tempp(1); | |
| if ttk<=0 | |
| tempp(1)=1; | |
| end | |
| clear onsetp; | |
| onsetp=tempp; | |
| clear tempp; | |
| if isempty(dicron),return;end | |
| sigLen=length(dicron); | |
| for diffIndex=1:sigLen | |
| if dicron(diffIndex)~=0 | |
| tempp(diffIndex)=dicron(diffIndex)-od; | |
| else | |
| tempp(diffIndex)=0; | |
| end | |
| end | |
| clear dicron; | |
| dicron=tempp; | |
| clear tempp; | |
| end | |
| function [mini,minip,maxi,maxip]=seeklocales(tempsig,tempbegin,tempend) | |
| tempMin=tempsig(tempbegin); | |
| tempMax=tempsig(tempbegin); | |
| minip=tempbegin; | |
| maxip=tempbegin; | |
| for j=tempbegin:tempend | |
| if tempsig(j)>tempMax | |
| tempMax=tempsig(j); | |
| maxip=j; | |
| elseif tempsig(j)<tempMin | |
| tempMin=tempsig(j); | |
| minip=j; | |
| end | |
| end | |
| mini=tempMin; | |
| maxi=tempMax; | |
| end | |
| function [dicron]=seekdicrotic(tempdiff) | |
| izcMin=0; | |
| izcMax=0; | |
| itemp=3; | |
| tempLen=length(tempdiff)-3; | |
| dicron=0; | |
| tempdiff=smooth(tempdiff); | |
| while itemp<=tempLen | |
| if (tempdiff(itemp)*tempdiff(itemp+1))<=0 | |
| if tempdiff(itemp-2)<0 | |
| if tempdiff(itemp+2)>=0 | |
| izcMin=izcMin+1; | |
| tzcMin(izcMin)=itemp; | |
| end | |
| end | |
| % if tempdiff(itemp-2)>0 | |
| % if tempdiff(itemp+2)<=0 | |
| % izcMax=izcMax+1; | |
| % tzcMax(izcMax)=itemp; | |
| % end | |
| % end | |
| end | |
| itemp=itemp+1; | |
| end | |
| if izcMin==0 %big inflection | |
| itemp=3; | |
| tempMin=tempdiff(itemp); | |
| itempMin=itemp; | |
| while itemp<tempLen | |
| if tempdiff(itemp)<tempMin | |
| tempMin=tempdiff(itemp); | |
| itempMin=itemp; | |
| end | |
| itemp=itemp+1; | |
| end | |
| itemp=itempMin+1; | |
| while itemp<tempLen | |
| if tempdiff(itemp+1)<=tempdiff(itemp-1) | |
| dicron=itemp; | |
| return; | |
| end | |
| itemp=itemp+1; | |
| end | |
| elseif izcMin==1 | |
| dicron=tzcMin(izcMin); | |
| return; | |
| else | |
| itemp=tzcMin(1); | |
| tempMax=tempdiff(itemp); | |
| itempMax=itemp; | |
| while itemp<tempLen | |
| if tempdiff(itemp)>tempMax | |
| tempMax=tempdiff(itemp); | |
| itempMax=itemp; | |
| end | |
| itemp=itemp+1; | |
| end | |
| for itemp=izcMin:-1:1 | |
| if tzcMin(itemp)<itempMax | |
| dicron=tzcMin(itemp); | |
| return; | |
| end | |
| end | |
| end | |
| end | |
| function [diap]=seekdiap(tempabp) | |
| diap=0; | |
| [tt,ti]=max(tempabp); | |
| if ti==0 | |
| diap=floor(length(tempabp)./2); | |
| else | |
| diap=ti; | |
| end | |
| end | |
| ","MATLAB" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","func/my_func_standardizing_PPG.m",".m","422","11","function PPG_buffer = my_func_standardizing_PPG(PPG_buffer) | |
| % Standardizing PPG into zero-mean and uni-variance. | |
| var_sig_PPG = var(PPG_buffer); | |
| if var_sig_PPG == 0 | |
| univar_sig_PPG = PPG_buffer; | |
| else | |
| univar_sig_PPG = sqrt(1/var_sig_PPG) * PPG_buffer; | |
| end | |
| zeromean_sig_PPG = univar_sig_PPG - mean(univar_sig_PPG); | |
| PPG_buffer = zeromean_sig_PPG; % univariance for PPG 30 sec segment | |
| end","MATLAB" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","func/my_func_prep_PPG_buffer.m",".m","554","17","function [PPG_buffer,fs_PPG] = my_func_prep_PPG_buffer(PPG_raw_buffer,fs_PPG) | |
| % Resample PPG to 50 Hz. | |
| if fs_PPG ~= 50 % Hz | |
| PPG_down = resample(PPG_raw_buffer,50,fs_PPG); | |
| fs_PPG = 50; | |
| else | |
| PPG_down = PPG_raw_buffer; | |
| end | |
| PPG_buffer = PPG_down(:); % Make sure PPG is column vector | |
| % Standardizing PPG in sub-function. | |
| PPG_buffer = my_func_standardizing_PPG(PPG_buffer); | |
| % Filter signal. | |
| [b, a] = butter(6,[0.5 20]/(fs_PPG/2)); % Bandpass filter. | |
| PPG_buffer = filtfilt(b, a, PPG_buffer); | |
| end","MATLAB" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","method_06/my_revise_run_wabp.m",".m","4330","109","function [r,ssf,my_avg0,A] = my_revise_run_wabp(abp,fs_abp) | |
| % Below was copied from Erick Andres Perez Alday's Github repository | |
| % "" physionetchallenges / matlab-classifier-2020 "": | |
| % https://github.com/physionetchallenges/matlab-classifier-2020/blob/master/Tools/PhysioNet-Cardiovascular-Signal-Toolbox-master/Tools/BP_Tools/run_wabp.m | |
| % WABP ABP waveform onset detector. | |
| % r = run_wabp(abp) obtains the onset time (in samples) | |
| % of each beat in the ABP waveform. | |
| % | |
| % In: ABP (125Hz sampled) | |
| % Out: Onset sample time | |
| % | |
| % Usage: | |
| % - ABP waveform must have units of mmHg | |
| % | |
| % Written by James Sun (xinsun@mit.edu) on Nov 19, 2005. This ABP onset | |
| % detector is adapted from Dr. Wei Zong's wabp.c. | |
| % | |
| % LICENSE: | |
| % This software is offered freely and without warranty under | |
| % the GNU (v3 or later) public license. See license file for | |
| % more information | |
| % Dong changed: input should be 250 Hz for filtering. | |
| %% Input checks | |
| % if nargin ~=1 | |
| % error('exactly 1 argment needed'); | |
| % end | |
| if size(abp,2)~=1 | |
| error('Input must be a <nx1> vector'); | |
| end | |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| % scale physiologic ABP | |
| offset = 1600; | |
| scale = 20; | |
| Araw = abp*scale-offset; | |
| % LPF | |
| A = filter([1 0 0 0 0 -2 0 0 0 0 1],[1 -2 1],Araw)/24+30; | |
| A = (A(4:end)+offset)/scale; % Takes care of 4 sample group delay | |
| % ------- Dong changed this: ------- | |
| A = A ./ std(A); % normalizing data is very important for my peak detection. | |
| A = A - mean(A); | |
| % Slope-sum function | |
| dypos = diff(A); | |
| dypos(dypos<0) = 0; | |
| % ssf = [0; 0; conv(ones(16,1),dypos)]; | |
| w = 16/125*fs_abp; % 125 Hz to 250 Hz. | |
| ssf = [0; 0; conv(ones(w,1),dypos)]; | |
| % Decision rule | |
| first_8sec = 8*fs_abp; | |
| % avg0 = sum(ssf(1:1000))/1000; % average of 1st 8 seconds (1000 samples) of SSF | |
| avg0 = sum(ssf(1:first_8sec))/first_8sec; | |
| Threshold0 = 3*avg0; % initial decision threshold | |
| % ignoring ""learning period"" for now | |
| lockout = 0; % lockout >0 means we are in refractory | |
| timer = 0; | |
| % z = zeros(100000,1); | |
| z = zeros(fs_abp*800,1); | |
| counter = 0; | |
| % Dong: copied from wabp.c, 02/27/2020. % Dong change here. 02/27/2020. | |
| TmDEF = 0.25; %5;% Dong change here. 02/27/2020. | |
| max_min_thres = 0.1; %10;% Dong change here. 02/27/2020. | |
| my_avg0 = zeros(size(abp));% Dong change here. 02/27/2020. | |
| step_adjust_thres = 0.025; % it was 0.1 % Dong change here. 02/27/2020. | |
| % for t = 50:length(ssf)-17 | |
| for t = round(0.4*fs_abp):length(ssf)-w-1 | |
| lockout = lockout - 1; | |
| timer = timer + 1; % Timer used for counting time after previous ABP pulse | |
| if (lockout<1) & (ssf(t)>avg0+TmDEF) %(ssf(t)>avg0+5) % Not in refractory and SSF has exceeded threshold here % Dong change here. 02/27/2020. | |
| timer = 0; | |
| maxSSF = max(ssf(t:t+w)); % Find local max of SSF | |
| minSSF = min(ssf(t-w:t)); % Find local min of SSF | |
| if maxSSF > (minSSF + max_min_thres) %(minSSF + 10)% Dong change here. 02/27/2020. | |
| onset = 0.01*maxSSF ; % Onset is at the time in which local SSF just exceeds 0.01*maxSSF | |
| tt = t-w:t; | |
| dssf = ssf(tt) - ssf(tt-1); | |
| BeatTime = find(dssf<onset,1,'last')+t-w-1; | |
| counter = counter+1; | |
| if isempty(BeatTime) | |
| counter = counter-1; | |
| else | |
| z(counter) = BeatTime; | |
| end | |
| Threshold0 = Threshold0 + step_adjust_thres*(maxSSF - Threshold0); % adjust threshold | |
| avg0 = Threshold0 / 3; % adjust avg | |
| lockout = round(32/125*fs_abp); % lock so prevent sensing right after detection (refractory period) | |
| end | |
| end | |
| if timer > round(312/125*fs_abp) % Lower threshold if no pulse detection for a while | |
| Threshold0 = Threshold0 - 0.1; %Threshold0 - 1; % Dong change here. 02/27/2020. | |
| avg0 = Threshold0/3; | |
| end | |
| my_avg0(t,1) = avg0+TmDEF; % % Dong change here. 02/27/2020. | |
| end | |
| r = z(find(z))-2; | |
| end","MATLAB" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","method_06/my_Elgendi_2013_method_III_peakdet.m",".m","1028","22","function [output_Elgendi_3_2013] = my_Elgendi_2013_method_III_peakdet(raw_PPG,fs_PPG) | |
| % ------------------------------------------------------------------------- | |
| % This peak detection function was mentioned in this paper: | |
| % Elgendi, Mohamed, et al. | |
| % ""Systolic peak detection in acceleration photoplethysmograms measured from | |
| % emergency responders in tropical conditions."" PLoS One 8.10 (2013): e76585. | |
| % | |
| [r,ssf,my_avg0,A] = my_revise_run_wabp(raw_PPG,fs_PPG); | |
| % ------------------------------------------------------------------------- | |
| if isempty(r) | |
| HR_Elgendi_3_2013 = 0; % there is no peak location. | |
| r = 1; | |
| else | |
| HR_Elgendi_3_2013 = 60 * fs_PPG ./ diff(r); % calculate the HR. | |
| end | |
| A = [A;0;0;0;]; % add zero | |
| A(1:6) = A(7); % first six plots are all high amplitude. | |
| output_Elgendi_3_2013 = struct('PPG_peak_loc_Elgendi_3_2013',r,... | |
| 'HR_Elgendi_3_2013',HR_Elgendi_3_2013,... | |
| 'filtered_PPG_Elgendi_3_2013',A,... | |
| 'thres_Elgendi_3_2013',my_avg0); | |
| end","MATLAB" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","method_03_and_04/my_Elgendi_2013_method_I_peakdet.m",".m","3762","111","function [output_Elgendi_1_2013] = my_Elgendi_2013_method_I_peakdet(raw_PPG, delta, fs_PPG) | |
| % ------------------------------------------------------------------------- | |
| % Dong add this on 02/25/2020, based on this paper: | |
| % Elgendi, Mohamed, et al. | |
| % ""Systolic peak detection in acceleration photoplethysmograms measured from | |
| % emergency responders in tropical conditions."" PLoS One 8.10 (2013): e76585. | |
| % | |
| % (1): bandpass filter (0.5-8Hz) | |
| [b, a] = butter(6,[0.5 8]/(fs_PPG/2)); % bandpass filter 0.5-10Hz, changed from 0.5-20 to 0.5-9 Hz at 11/21/2018 | |
| raw_PPG = filtfilt(b, a, raw_PPG); % -> AC component | |
| raw_PPG = raw_PPG ./ std(raw_PPG); % normalizing data is very important for my peak detection. | |
| raw_PPG = raw_PPG - mean(raw_PPG); | |
| debugging_plot_flag = false; % only for plotting debugging figures. | |
| % ------------------------------------------------------------------------- | |
| % Below code is copied from: http://billauer.co.il/peakdet.html | |
| % PEAKDET Detect peaks in a vector | |
| % [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local | |
| % maxima and minima (""peaks"") in the vector V. | |
| % MAXTAB and MINTAB consists of two columns. Column 1 | |
| % contains indices in V, and column 2 the found values. | |
| % | |
| % With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices | |
| % in MAXTAB and MINTAB are replaced with the corresponding | |
| % X-values. | |
| % | |
| % A point is considered a maximum peak if it has the maximal | |
| % value, and was preceded (to the left) by a value lower by | |
| % DELTA. | |
| % Eli Billauer, 3.4.05 (Explicitly not copyrighted). | |
| % This function is released to the public domain; Any use is allowed. | |
| maxtab = []; | |
| mintab = []; | |
| raw_PPG = raw_PPG(:); % Just in case this wasn't a proper vector | |
| % if nargin < 3 | |
| x = (1:length(raw_PPG))'; | |
| % else | |
| % x = x(:); | |
| % if length(raw_PPG)~= length(x) | |
| % error('Input vectors v and x must have same length'); | |
| % end | |
| % end | |
| if (length(delta(:)))>1 | |
| error('Input argument DELTA must be a scalar'); | |
| end | |
| if delta <= 0 | |
| error('Input argument DELTA must be positive'); | |
| end | |
| mn = Inf; mx = -Inf; | |
| mnpos = NaN; mxpos = NaN; | |
| lookformax = 1; | |
| for i=1:length(raw_PPG) | |
| this = raw_PPG(i); | |
| if this > mx, mx = this; mxpos = x(i); end | |
| if this < mn, mn = this; mnpos = x(i); end | |
| if lookformax | |
| if this < mx-delta | |
| maxtab = [maxtab ; mxpos mx]; | |
| mn = this; mnpos = x(i); | |
| lookformax = 0; | |
| end | |
| else | |
| if this > mn+delta | |
| mintab = [mintab ; mnpos mn]; | |
| mx = this; mxpos = x(i); | |
| lookformax = 1; | |
| end | |
| end | |
| end | |
| if isempty(maxtab) | |
| HR_Elgendi_1_max_2009 = 0; % there is no peak location. | |
| peak_loc_max = 1; | |
| else | |
| peak_loc_max = maxtab(:,1); | |
| HR_Elgendi_1_max_2009 = 60 * fs_PPG ./ diff(peak_loc_max); % calculate the HR. | |
| end | |
| if isempty(mintab) | |
| HR_Elgendi_1_min_2009 = 0; % there is no peak location. | |
| peak_loc_min = 1; | |
| else | |
| peak_loc_min = mintab(:,1); | |
| HR_Elgendi_1_min_2009 = 60 * fs_PPG ./ diff(peak_loc_min); % calculate the HR. | |
| end | |
| output_Elgendi_1_2013 = struct('filtered_PPG_Elgendi_1_2013',raw_PPG,... | |
| 'PPG_peak_loc_Elgendi_1_max_2013',peak_loc_max,... | |
| 'PPG_peak_loc_Elgendi_1_min_2013',peak_loc_min,... | |
| 'HR_Elgendi_1_max_2013',HR_Elgendi_1_max_2009,... | |
| 'HR_Elgendi_1_min_2013',HR_Elgendi_1_min_2009); | |
| if debugging_plot_flag % debugging plot | |
| figure; | |
| plot(x,raw_PPG);hold on; | |
| plot(peak_loc_max,raw_PPG(peak_loc_max),'ro'); | |
| plot(peak_loc_min,raw_PPG(peak_loc_min),'go'); | |
| end | |
| end","MATLAB" | |
| "Biosensors","Cassey2016/PPG_Peak_Detection","method_08_and_09/my_Vadrevu_2019_peakdet.m",".m","10598","329","function [output_Vadrevu_1_2019,output_Vadrevu_2_2019] = my_Vadrevu_2019_peakdet(PPG_buffer,fs_PPG) | |
| % ========================================================================= | |
| % This is my implementation of this paper: | |
| % | |
| % Vadrevu, Simhadri, and M. Sabarimalai Manikandan. | |
| % ""A robust pulse onset and peak detection method for automated PPG signal | |
| % analysis system."" IEEE Transactions on Instrumentation and Measurement | |
| % 68.3 (2018): 807-817. | |
| % | |
| % Implemented by Dong Han on 05/03/2020. | |
| % | |
| % Please cite our paper if you used this code: | |
| % Han, Dong, Syed K. Bashar, Jesús Lázaro, Fahimeh Mohagheghian, | |
| % Andrew Peitzsch, Nishat Nishita, Eric Ding, Emily L. Dickson, | |
| % Danielle DiMezza, Jessica Scott, Cody Whitcomb, Timothy P. Fitzgibbons, | |
| % David D. McManus, and Ki H. Chon. 2022. | |
| % ""A Real-Time PPG Peak Detection Method for Accurate Determination of | |
| % Heart Rate during Sinus Rhythm and Cardiac Arrhythmia"" | |
| % Biosensors 12, no. 2: 82. https://doi.org/10.3390/bios12020082 | |
| % | |
| % Please cite our paper if you used our code. Thank you. | |
| % ========================================================================= | |
| debug_flag = false; % decide to plot the paper figure or not. | |
| %% A. Stationary Wavelet Transform of PPG signal. | |
| % first, for the input length, you can know the maximum wavelet | |
| % decomposition level you can get: | |
| TYPE = '1D'; % extension method. | |
| MODE = 'zpd'; % zero extension. | |
| X = PPG_buffer; | |
| % based on your input signal length, you have to extend your input signal | |
| % to MATLAB suggested length. | |
| LEN = 45;%18; % 18 for fs_PPG 50, 45 for fs_PPG 125; for 30 sec input. | |
| YEXT = wextend(TYPE,MODE,X,LEN); % required by swt. | |
| sig = YEXT; | |
| % s = PPG_buffer; | |
| sLen = length(sig); | |
| wname = 'bior1.5'; | |
| L = wmaxlev(sLen,wname); | |
| % [swa,swd] = swt(s,3,'bior1.5'); % the author mentioned wavelet biorthogonal 1.5 (bior1.5) | |
| [swa,swd] = swt(sig,L,wname); % the author mentioned wavelet biorthogonal 1.5 (bior1.5) | |
| s1 = swd(3,:) + swd(4,:); | |
| s1 = s1(:); % make sure it is column vector. | |
| s2 = swd(5,:) + swd(6,:) + swd(7,:); | |
| s2 = s2(:); % make sure it is column vector. | |
| if debug_flag | |
| % if you want to debug the result. | |
| figure; | |
| t_plot = [1:length(sig)]'./fs_PPG; % | |
| subplot(5,1,1); | |
| plot(t_plot,sig); | |
| xlim([0 t_plot(end)]) | |
| ylabel('Orig'); | |
| title('Fig.3 in TIM 2019 paper'); | |
| subplot(5,1,2) | |
| plot(t_plot,(swd(1,:) + swd(2,:))) | |
| xlim([0 t_plot(end)]) | |
| ylabel('s_0'); | |
| subplot(5,1,3); | |
| plot(t_plot,s1); | |
| xlim([0 t_plot(end)]) | |
| ylabel('s_1'); | |
| subplot(5,1,4); | |
| plot(t_plot,s2); | |
| xlim([0 t_plot(end)]) | |
| ylabel('s_2'); | |
| subplot(5,1,5); | |
| plot(t_plot,swa(7,:)); | |
| xlim([0 t_plot(end)]) | |
| ylabel('a_7'); | |
| end | |
| %% B. Multiscale Sum and Products: | |
| p = s1 .* s2; | |
| p = p(:); | |
| if debug_flag | |
| % if you want to debug the result. | |
| figure; | |
| ax(1) = subplot(4,1,1); | |
| plot(t_plot,sig); | |
| xlim([0 t_plot(end)]) | |
| ylabel('Orig'); | |
| title('Fig.4 in TIM 2019 paper'); | |
| ax(2) = subplot(4,1,2); | |
| p1 = swd(1,:) .* swd(2,:) .* swd(3,:) .* swd(4,:) .* swd(5,:) .* swd(6,:) .* swd(7,:); | |
| plot(t_plot,p1); | |
| xlim([0 t_plot(end)]) | |
| ylabel('p_1'); | |
| ax(3) = subplot(4,1,3); | |
| p1 = swd(3,:) .* swd(4,:) .* swd(5,:) .* swd(6,:) .* swd(7,:); | |
| plot(t_plot,p1); | |
| xlim([0 t_plot(end)]) | |
| ylabel('p_2'); | |
| ax(4) = subplot(4,1,4); | |
| plot(t_plot,p); | |
| xlim([0 t_plot(end)]) | |
| ylabel('p'); | |
| linkaxes(ax,'x'); | |
| end | |
| %% C. Shannon Entropy Envelope Extraction | |
| eta = 0.01 + std(p); | |
| p_tilda = abs(p); | |
| p_tilda(p_tilda < eta) = 0; | |
| p_tilda = p_tilda(:); | |
| % normalize p_tilda: | |
| norm_p_tilda = (p_tilda - min(p_tilda)) ./ (max(p_tilda) - min(p_tilda)); | |
| norm_p_tilda = norm_p_tilda(:); | |
| se = NaN(size(norm_p_tilda)); | |
| for tttt = 1:size(norm_p_tilda,1) | |
| if norm_p_tilda(tttt) == 0 | |
| % from MATLAB page: https://www.mathworks.com/help/wavelet/ref/wentropy.html | |
| % log(0) = 0 | |
| % 0log(0) = 0. | |
| se(tttt) = 0; | |
| else | |
| se(tttt) = -1 * norm_p_tilda(tttt) .* log(norm_p_tilda(tttt)); | |
| end | |
| end | |
| % % method 1: CONV twice: | |
| filt_Len = floor(0.2 * fs_PPG); % 0.4 is better. 05/04/2020. | |
| % h = ones(filt_Len,1)/filt_Len; % A third-order filter has length 4 | |
| % s = conv(se,h,'same'); % return the same size as se | |
| % s = conv(s,h,'same'); % conv twice | |
| % method 2: FILTFILT. | |
| % for 4020, ii = 2, PPG is zero. | |
| if any(isnan(se)) | |
| % any sample is NaN. | |
| new_se = se; | |
| new_se(isnan(new_se)) = []; | |
| if isempty(new_se) | |
| % nothing left after removing NaN. | |
| HR_Vadrevu_1_2019 = 0; % there is no peak location. | |
| onset_zx = 1; | |
| HR_Vadrevu_2_2019 = 0; % there is no peak location. | |
| peak_zx = 1; | |
| filter_PPG = PPG_buffer; | |
| output_Vadrevu_1_2019 = struct('filtered_PPG_Vadrevu_2019',filter_PPG,... | |
| 'PPG_peak_loc_Vadrevu_1_2019',onset_zx,... | |
| 'HR_Vadrevu_1_2019',HR_Vadrevu_1_2019); | |
| output_Vadrevu_2_2019 = struct('filtered_PPG_Vadrevu_2019',filter_PPG,... | |
| 'PPG_peak_loc_Vadrevu_2_2019',peak_zx,... | |
| 'HR_Vadrevu_2_2019',HR_Vadrevu_2_2019); | |
| return | |
| else | |
| % part of data is NaN, maybe I should fill zeros in it? | |
| keyboard; | |
| end | |
| end | |
| b = ones(filt_Len,1); | |
| a = -1; | |
| s = filtfilt(b, a, se); % -> AC component | |
| %% D. Pulse Peak and Onset Determination. | |
| % 1. Gaussian derivative kernel: | |
| sigma_1 = floor(0.05 * fs_PPG); % 0.05 mentioned in the paper. | |
| M = floor(2 * fs_PPG); % 2 mentioned in the paper. | |
| g = gausswin(M,sigma_1); % size should be 250 if Fs = 125. | |
| h_d = diff(g); % g(m+1) - g(m). | |
| z = conv(s,h_d,'same'); | |
| % % My conv function did not work. | |
| % temp_z = zeros(size(s,1),1); | |
| % for nnnn = 1:size(s,1) | |
| % for mmmm = 1:size(g,1)-1 | |
| % if (nnnn-mmmm+1 > 0) | |
| % % h_d(mmmm) = g(mmmm+1) - g(mmmm); | |
| % temp_z(nnnn) = temp_z(nnnn) + s(mmmm) * h_d(nnnn-mmmm+1); | |
| % end | |
| % end | |
| % end | |
| DownZCi = @(v) find(v(1:end-1) >= 0 & v(2:end) < 0); % Returns Down Zero-Crossing Indices. https://www.mathworks.com/matlabcentral/answers/267222-easy-way-of-finding-zero-crossing-of-a-function | |
| zx = DownZCi(z); % negative zero crossing point. | |
| % peak correction algorithm for onset: | |
| search_intv = floor(0.1 * fs_PPG / 2); % w/2 | |
| onset_zx = NaN(size(zx)); | |
| for zz = 1:size(zx,1) | |
| temp_zx = zx(zz); | |
| if temp_zx - search_intv > 0 % not exceed signal limit. | |
| if temp_zx + search_intv <= size(sig,1) | |
| temp_PPG = sig(temp_zx - search_intv : temp_zx + search_intv); | |
| [~,I] = min(temp_PPG); | |
| if isempty(I) ~= 1 | |
| adj_loc = temp_zx - search_intv + I - 1; | |
| else | |
| % no local minimum. | |
| adj_loc = temp_zx; | |
| end | |
| onset_zx(zz) = adj_loc; | |
| else | |
| % right interval exceed signal length. | |
| onset_zx(zz) = zx(zz); | |
| end | |
| else | |
| % left interval exceed index 1. | |
| onset_zx(zz) = zx(zz); | |
| end | |
| end | |
| % find peak: | |
| peak_zx = NaN(size(onset_zx,1)-1,1); % one sample smaller. | |
| for zz = 2:size(onset_zx,1) | |
| temp_onset_1 = onset_zx(zz-1); | |
| temp_onset_2 = onset_zx(zz); | |
| temp_PPG = sig(temp_onset_1:temp_onset_2); | |
| [~,I] = max(temp_PPG); | |
| if isempty(I) ~= 1 | |
| peak_zx(zz-1) = temp_onset_1 + I - 1; % peak is one sample size smaller. | |
| else | |
| peak_zx(zz-1) = onset_zx(zz); | |
| end | |
| end | |
| % prepare to output signal: | |
| filter_PPG = z(LEN+1:end-LEN); | |
| remove_left = find(onset_zx < LEN+1); | |
| if isempty(remove_left) ~= 1 | |
| onset_zx(remove_left) = []; | |
| end | |
| remove_right = find(onset_zx > size(z,1) - LEN); | |
| if isempty(remove_right) ~= 1 | |
| onset_zx(remove_right) = []; | |
| end | |
| onset_zx = onset_zx - LEN; % shifted. | |
| remove_left = find(peak_zx < LEN+1); | |
| if isempty(remove_left) ~= 1 | |
| peak_zx(remove_left) = []; | |
| end | |
| remove_right = find(peak_zx > size(z,1) - LEN); | |
| if isempty(remove_right) ~= 1 | |
| peak_zx(remove_right) = []; | |
| end | |
| peak_zx = peak_zx - LEN; | |
| if debug_flag | |
| % if you want to debug the result. | |
| figure; | |
| ax(1) = subplot(7,1,1); | |
| plot(t_plot,sig); | |
| xlim([0 t_plot(end)]) | |
| ylabel('Orig'); | |
| title('Fig.5 in TIM 2019 paper'); | |
| ax(2) = subplot(7,1,2); | |
| plot(t_plot,p); | |
| xlim([0 t_plot(end)]) | |
| ylabel('p'); | |
| ax(3) = subplot(7,1,3); | |
| plot(t_plot,norm_p_tilda); | |
| xlim([0 t_plot(end)]) | |
| ylabel('p_th'); | |
| ax(4) = subplot(7,1,4); | |
| plot(t_plot,se); | |
| xlim([0 t_plot(end)]) | |
| ylabel('se'); | |
| ax(5) = subplot(7,1,5); | |
| plot(t_plot,s); | |
| xlim([0 t_plot(end)]) | |
| ylabel('s'); | |
| ax(6) = subplot(7,1,6); | |
| plot(t_plot,z); | |
| hold on; | |
| plot(t_plot(zx),z(zx),'ro'); | |
| xlim([0 t_plot(end)]); | |
| ylabel('z'); | |
| ax(7) = subplot(7,1,7); | |
| plot(t_plot,sig); | |
| hold on; | |
| plot(t_plot(onset_zx),sig(onset_zx),'go'); | |
| plot(t_plot(peak_zx),sig(peak_zx),'ro'); | |
| xlim([0 t_plot(end)]) | |
| ylabel('orig with peak'); | |
| linkaxes(ax,'x'); | |
| end | |
| if isempty(onset_zx) | |
| HR_Vadrevu_1_2019 = 0; % there is no peak location. | |
| onset_zx = 1; | |
| else | |
| HR_Vadrevu_1_2019 = 60 * fs_PPG ./ diff(onset_zx); % calculate the HR. | |
| end | |
| if isempty(peak_zx) | |
| HR_Vadrevu_2_2019 = 0; % there is no peak location. | |
| peak_zx = 1; | |
| else | |
| HR_Vadrevu_2_2019 = 60 * fs_PPG ./ diff(peak_zx); % calculate the HR. | |
| end | |
| output_Vadrevu_1_2019 = struct('filtered_PPG_Vadrevu_2019',filter_PPG,... | |
| 'PPG_peak_loc_Vadrevu_1_2019',onset_zx,... | |
| 'HR_Vadrevu_1_2019',HR_Vadrevu_1_2019); | |
| output_Vadrevu_2_2019 = struct('filtered_PPG_Vadrevu_2019',filter_PPG,... | |
| 'PPG_peak_loc_Vadrevu_2_2019',peak_zx,... | |
| 'HR_Vadrevu_2_2019',HR_Vadrevu_2_2019); | |
| end | |
| ","MATLAB" | |