Heart rate detection in ECG - matlab

Question

I need help to find Heart rate in 2 ECGs signals that I'm analyzing.
This is what I did since now:
Normalization and mean value removing
50Hz filtering band stop
filtering with 0.5 Hz Butterworth high pass
R-peaks filtering 15Hz and 20Hz Butterworth hight and low pass
Now, I'm basing my analysis on this paper but I'm stuck on Heart rate detection algorithms with Autocorrelation of energy signal / Thresholding of energy signal. I can't understand how to get the heart rate, can anyone tell me (with a code example please). Here is what I'm doing:
valvular_heart_disease = load('disease.mat');
figure;plot(valvular_heart_disease.val);
healthy_control = load('healthy.mat');
figure;plot(healthy_control.ecg);
fs = 1000; % Sampling frequency - Hz
n_dis = length(valvular_heart_disease.val); % length
n_heal = length(healthy_control.ecg);
timevec_dis = (0:n_dis-1)/fs; % time vector
timevec_heal = (0:n_heal-1)/fs; % time vector
% Detrend data vhd
detrend_data_vhd = detrend(valvular_heart_disease.val);
% Normalize data vhd
max_value_vhd = max(detrend_data_vhd);
normalized_vhd = detrend_data_vhd/max_value_vhd;
% Detrend data hc
detrend_data_hc = detrend(healthy_control.ecg);
% Normalize data hc
max_value_hc = max(detrend_data_hc);
normalized_hc = detrend_data_hc/max_value_hc;
%figure;plot(normalized_vhd);
%%%%%%%%%%% FILTERING %%%%%%%%%%%
% Notch def band stop
wo = 50/(fs/2);
bw = wo/35;
[b,a] = iirnotch(wo,bw);
% Butter High Pass
[z,p] = butter(2,0.5/(fs/2),'high');
% Butter low-high
%fcutlow=15; %low cut frequency in Hz
%fcuthigh=20; %high cut frequency in Hz
%[c,d]=butter(4,[fcutlow,fcuthigh]/(fs/2),'bandpass');
[c,d] =butter(2,15/(fs/2),'low');
[h,i] =butter(2,20/(fs/2),'high');
% Notch
ecg_vhd_notch = filter(b,a,normalized_vhd);
ecg_hc_notch = filter(b,a,normalized_hc);
% Butter
ecg_vhd_btr = filter(z,p,ecg_vhd_notch);
ecg_hc_btr = filter(z,p,ecg_hc_notch);
% Butter low-high
%l
ecg_vhd_low = filter(c,d,ecg_vhd_btr);
ecg_hc_low = filter(c,d,ecg_hc_btr);
%h
ecg_vhd_high = filter(h,i,ecg_vhd_low);
ecg_hc_high = filter(h,i,ecg_hc_low);
% Plotting Notch
figure
subplot(2,1,1)
plot(timevec_dis, normalized_vhd, 'b', timevec_dis, ecg_vhd_notch, 'r');
title('Segnale VHD Notch')
xlabel('sec')
ylabel('amp')
figure
subplot(2,1,1)
plot(timevec_heal, normalized_hc, 'b', timevec_heal, ecg_hc_notch, 'r');
title('Segnale HC Notch')
% Plotting after butter
figure
subplot(2,1,1)
plot(timevec_dis, normalized_vhd, 'b', timevec_dis, ecg_vhd_btr, 'r');
title('Segnale VHD Butter')
figure
subplot(2,1,1)
plot(timevec_heal, normalized_hc, 'b', timevec_heal, ecg_hc_btr, 'r');
title('Segnale HC Butter')
xlabel('time')
ylabel('amplitude')
% filtering with high pass and low pass Butterworth filters
% with cut-off frequencies 15 Hz and 20 Hz
figure
subplot(2,1,1)
plot(timevec_dis, normalized_vhd, 'b', timevec_dis, ecg_vhd_high, 'r');
title('Segnale VHD Butter')
figure
subplot(2,1,1)
plot(timevec_heal, normalized_hc, 'b', timevec_heal, ecg_hc_high, 'r');
title('Segnale HC Butter')
% Heart rate detection
et = abs(ecg_hc_high).^2;
plot(timevec_heal, et)
ac = xcorr(et);
plot(ac)
th = 2*mean(et);
[pks, locs] = findpeaks(j);
% what now?
Rwave = pcks>th;
% what now?

Answer 1

I suggest searching for wavelet-based methods. They are the most commonly used for this task
This paper might help. (The main author was a lecturer of mine at the Research Institute where I studied my Master's Degree)