對信號的峰值進行分析
Step1.
簡單的尋找最大值點
load sunspot.dat
year=sunspot(:,1);
relNums=sunspot(:,2);
findpeaks(relNums,year);
xlabel('Year');
ylabel('Sunspot Number')
title('Find All Peaks');
圖片.png
Step2.
計算峰值點的距離
findpeaks(relNums,year,'MinPeakProminence',40);
xlabel('Year');
ylabel('Sunspot Number')
title('Find Prominent Peaks');
圖片.png
先用直方圖分析
figure
[pks, locs] = findpeaks(relNums,year,'MinPeakProminence',40);
peakInterval = diff(locs);
hist(peakInterval);
grid on
xlabel('Year Intervals');
ylabel('Frequency of Occurrence')
title('Histogram of Peak Intervals (years)')
AverageDistance_Peaks = mean(diff(locs))
圖片.png
Step3.
查看截止或者飽和信號的峰值點
load clippedpeaks.mat
figure
% Show all peaks in the first plot
ax(1) = subplot(2,1,1);
findpeaks(saturatedData);
xlabel('Samples')
ylabel('Amplitude')
title('Detecting Saturated Peaks')
% Specify a minimum excursion in the second plot
ax(2) = subplot(2,1,2);
findpeaks(saturatedData,'threshold',5)
xlabel('Samples');
ylabel('Amplitude')
title('Filtering Out Saturated Peaks')
% link and zoom in to show the changes
linkaxes(ax(1:2),'xy');
axis(ax,[50 70 0 250])
圖片.png
可以通過增加門限進行設(shè)定，否則對于一個平坦的峰恤煞，上升沿的第一個點會被認(rèn)為是峰值
step4.
測量峰值幅度
用ECG信號舉例說明
load noisyecg.mat
t = 1:length(noisyECG_withTrend);
figure
plot(t,noisyECG_withTrend)
title('Signal with a Trend')
xlabel('Samples');
ylabel('Voltage(mV)')
legend('Noisy ECG Signal')
grid on
圖片.png
step5.
去除信號趨勢
[p,s,mu] = polyfit((1:numel(noisyECG_withTrend))',noisyECG_withTrend,6);
f_y = polyval(p,(1:numel(noisyECG_withTrend))',[],mu);
ECG_data = noisyECG_withTrend - f_y; % Detrend data
figure
plot(t,ECG_data); grid on
ax = axis; axis([ax(1:2) -1.2 1.2])
title('Detrended ECG Signal')
xlabel('Samples'); ylabel('Voltage(mV)')
legend('Detrended ECG Signal')
圖片.png
下圖是一個標(biāo)準(zhǔn)的ECG信號圖
圖片.png
Step6.
感興趣的峰值點門限战转，ECG主要的三個部分娃承，包括S波，Q波薛匪，R波
R波可以通過設(shè)定0.5mv的門限檢出
[~,locs_Rwave] = findpeaks(ECG_data,'MinPeakHeight',0.5,...
'MinPeakDistance',200);
對于S波，要設(shè)置適當(dāng)門限
ECG_inverted = -ECG_data;
[~,locs_Swave] = findpeaks(ECG_inverted,'MinPeakHeight',0.5,...
'MinPeakDistance',200);
然后標(biāo)記出來如下
figure
hold on
plot(t,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;
legend('ECG Signal','R-waves','S-waves');
xlabel('Samples'); ylabel('Voltage(mV)')
title('R-wave and S-wave in Noisy ECG Signal')
圖片.png
為了定位Q波脓鹃，先濾波逸尖，采用Savitzky-Golay濾波器
smoothECG = sgolayfilt(ECG_data,7,21);
figure
plot(t,ECG_data,'b',t,smoothECG,'r'); grid on
axis tight;
xlabel('Samples'); ylabel('Voltage(mV)');
legend('Noisy ECG Signal','Filtered Signal')
title('Filtering Noisy ECG Signal')
圖片.png
查找Q波如下
[~,min_locs] = findpeaks(-smoothECG,'MinPeakDistance',40);
% Peaks between -0.2mV and -0.5mV
locs_Qwave = min_locs(smoothECG(min_locs)>-0.5 & smoothECG(min_locs)<-0.2);
figure
hold on
plot(t,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('Samples'); ylabel('Voltage(mV)')
ax = axis; axis([0 1850 -1.1 1.1])
legend('Smooth ECG signal','Q-wave','R-wave','S-wave');
圖片.png
查看噪聲信號和平滑后哦信號的誤差
% Values of the Extrema
[val_Qwave, val_Rwave, val_Swave] = deal(smoothECG(locs_Qwave), smoothECG(locs_Rwave), smoothECG(locs_Swave));
meanError_Qwave = mean((noisyECG_withTrend(locs_Qwave) - val_Qwave))
meanError_Rwave = mean((noisyECG_withTrend(locs_Rwave) - val_Rwave))
meanError_Swave = mean((noisyECG_withTrend(locs_Swave) - val_Swave))
圖片.png
峰值分析
主要分析上升時間，下降時間等
avg_riseTime = mean(locs_Rwave-locs_Qwave); % Average Rise time
avg_fallTime = mean(locs_Swave-locs_Rwave); % Average Fall time
avg_riseLevel = mean(val_Rwave-val_Qwave); % Average Rise Level
avg_fallLevel = mean(val_Rwave-val_Swave); % Average Fall Level
helperPeakAnalysisPlot(t,smoothECG,...
locs_Qwave,locs_Rwave,locs_Swave,...
val_Qwave,val_Rwave,val_Swave,...
avg_riseTime,avg_fallTime,...
avg_riseLevel,avg_fallLevel)
圖片.png
總結(jié)：
- 學(xué)會幾個函數(shù)的使用
findpeaks瘸右， 查找峰值點
linkaxes娇跟， 關(guān)聯(lián)子圖的坐標(biāo)軸
polyfit， 擬合
polyval太颤，擬合后的應(yīng)用
sgolayfilt苞俘，濾波器
deal， 輸入到輸出的賦值
helperPeakAnalysisPlot龄章，peak峰值點繪圖吃谣，內(nèi)部函數(shù)看不到介紹
- 查找峰值點的一般方法
如何設(shè)置參數(shù)范圍大小
針對ECG如何獲取Q,R,S波峰位置
