matlab对心电信号通过分析斜率、幅度和宽度识别来实现QRS复波qrs复波检测

Question

给定一个已知的ecg.mat信号文件，编写matlab程序实现算法通过分析斜率、幅度和宽度识别QRS复波、R-R间期检测、以及心率检测，具体滤波为经过低通、高通、滤波、平方、高通、滑动平均、设置阈值然后得到QRS复波特征

Answer 1

自己调整吧

% 加载心电信号
load('ecg.mat');
ecg = val;

% 滤波器设计和参数设置
fs = 1000; % 采样频率
fc_lowpass = 30; % 低通滤波截止频率
fc_highpass = 5; % 高通滤波截止频率
filter_order = 4; % 滤波器阶数

% 低通滤波器设计
[b_low, a_low] = butter(filter_order, fc_lowpass / (fs/2), 'low');

% 高通滤波器设计
[b_high, a_high] = butter(filter_order, fc_highpass / (fs/2), 'high');

% 滤波
ecg_filtered = filtfilt(b_high, a_high, filtfilt(b_low, a_low, ecg));

% 平方
ecg_squared = ecg_filtered .^ 2;

% 高通滤波
fc_highpass2 = 0.5; % 第二次高通滤波截止频率
[b_high2, a_high2] = butter(filter_order, fc_highpass2 / (fs/2), 'high');
ecg_highpass2 = filtfilt(b_high2, a_high2, ecg_squared);

% 滑动平均
window_size = 100; % 滑动平均窗口大小
ecg_moving_average = movmean(ecg_highpass2, window_size);

% 设置阈值
threshold = 0.05 * max(ecg_moving_average); % 阈值为峰值的百分之五

% 寻找QRS复波特征
qrs_complex = ecg_moving_average > threshold;

% R-R间期检测
[peaks, locs] = findpeaks(ecg_moving_average, 'MinPeakHeight', threshold);

% 计算心率
rr_intervals = diff(locs); % R-R间期
heart_rate = 60 / mean(rr_intervals) % 平均心率

% 绘制心电信号和检测结果
t = (0:length(ecg)-1) / fs; % 时间轴
figure;
plot(t, ecg, 'b');
hold on;
plot(t, ecg_moving_average, 'r');
plot(t(qrs_complex), ecg_moving_average(qrs_complex), 'go');
xlabel('Time (s)');
ylabel('Amplitude');
legend('ECG Signal', 'Moving Average', 'QRS Complex');