这学期的课程选择神经网络。最后的作业处理ECG信号,并利用神经网络识别。
1 ECG引进和阅读ECG信号
1)ECG介绍
详细ECG背景应用就不介绍了,大家能够參考百度 谷歌。仅仅是简单说下ECG的结构:
一个完整周期的ECG信号有 QRS P T 波组成,不同的人相应不用的波形,同一个人在不同的阶段波形也不同。我们须要依据各个波形的特点,提取出相应的特征,对不同的人进行身份识别。
2)ECG信号读取
首先须要到MIT-BIH数据库中下载ECG信号,具体的下载地址与程序读取内容介绍能够參考一下地址(讲述的非常具体):http://blog.csdn.net/chenyusiyuan/article/details/2027887。
读代替码(基于MATLAB)例如以下:
clc; clear all; %------ SPECIFY DATA ------------------------------------------------------ %%选择文件名称 stringname='111'; %选择你要处理的信号点数 points=10000; PATH= 'F:\ECG\MIT-BIH database directory'; % path, where data are saved HEADERFILE= strcat(stringname,'.hea'); % header-file in text format ATRFILE= strcat(stringname,'.atr'); % attributes-file in binary format DATAFILE=strcat(stringname,'.dat'); % data-file SAMPLES2READ=points; % number of samples to be read % in case of more than one signal: % 2*SAMPLES2READ samples are read %------ LOAD HEADER DATA -------------------------------------------------- fprintf(1,'\\n$> WORKING ON %s ...\n', HEADERFILE); signalh= fullfile(PATH, HEADERFILE); fid1=fopen(signalh,'r'); z= fgetl(fid1); A= sscanf(z, '%*s %d %d %d',[1,3]); nosig= A(1); % number of signals sfreq=A(2); % sample rate of data clear A; for k=1:nosig z= fgetl(fid1); A= sscanf(z, '%*s %d %d %d %d %d',[1,5]); dformat(k)= A(1); % format; here only 212 is allowed gain(k)= A(2); % number of integers per mV bitres(k)= A(3); % bitresolution zerovalue(k)= A(4); % integer value of ECG zero point firstvalue(k)= A(5); % first integer value of signal (to test for errors) end; fclose(fid1); clear A; %------ LOAD BINARY DATA -------------------------------------------------- if dformat~= [212,212], error('this script does not apply binary formats different to 212.'); end; signald= fullfile(PATH, DATAFILE); % data in format 212 fid2=fopen(signald,'r'); A= fread(fid2, [3, SAMPLES2READ], 'uint8')'; % matrix with 3 rows, each 8 bits long, = 2*12bit fclose(fid2); M2H= bitshift(A(:,2), -4); M1H= bitand(A(:,2), 15); PRL=bitshift(bitand(A(:,2),8),9); % sign-bit PRR=bitshift(bitand(A(:,2),128),5); % sign-bit M( : , 1)= bitshift(M1H,8)+ A(:,1)-PRL; M( : , 2)= bitshift(M2H,8)+ A(:,3)-PRR; if M(1,:) ~= firstvalue, error('inconsistency in the first bit values'); end; switch nosig case 2 M( : , 1)= (M( : , 1)- zerovalue(1))/gain(1); M( : , 2)= (M( : , 2)- zerovalue(2))/gain(2); TIME=(0:(SAMPLES2READ-1))/sfreq; case 1 M( : , 1)= (M( : , 1)- zerovalue(1)); M( : , 2)= (M( : , 2)- zerovalue(1)); M=M'; M(1)=[]; sM=size(M); sM=sM(2)+1; M(sM)=0; M=M'; M=M/gain(1); TIME=(0:2*(SAMPLES2READ)-1)/sfreq; otherwise % this case did not appear up to now! % here M has to be sorted!!! disp('Sorting algorithm for more than 2 signals not programmed yet!'); end; clear A M1H M2H PRR PRL; fprintf(1,'\\n$> LOADING DATA FINISHED \n'); %------ LOAD ATTRIBUTES DATA ---------------------------------------------- atrd= fullfile(PATH, ATRFILE); % attribute file with annotation data fid3=fopen(atrd,'r'); A= fread(fid3, [2, inf], 'uint8')'; fclose(fid3); ATRTIME=[]; ANNOT=[]; sa=size(A); saa=sa(1); i=1; while i<=saa annoth=bitshift(A(i,2),-2); if annoth==59 ANNOT=[ANNOT;bitshift(A(i+3,2),-2)]; ATRTIME=[ATRTIME;A(i+2,1)+bitshift(A(i+2,2),8)+... bitshift(A(i+1,1),16)+bitshift(A(i+1,2),24)]; i=i+3; elseif annoth==60 % nothing to do! elseif annoth==61 % nothing to do! elseif annoth==62 % nothing to do! elseif annoth==63 hilfe=bitshift(bitand(A(i,2),3),8)+A(i,1); hilfe=hilfe+mod(hilfe,2); i=i+hilfe/2; else ATRTIME=[ATRTIME;bitshift(bitand(A(i,2),3),8)+A(i,1)]; ANNOT=[ANNOT;bitshift(A(i,2),-2)]; end; i=i+1; end; ANNOT(length(ANNOT))=[]; % last line = EOF (=0) ATRTIME(length(ATRTIME))=[]; % last line = EOF clear A; ATRTIME= (cumsum(ATRTIME))/sfreq; ind= find(ATRTIME <= TIME(end)); ATRTIMED= ATRTIME(ind); ANNOT=round(ANNOT); ANNOTD= ANNOT(ind); %------ DISPLAY DATA ------------------------------------------------------ figure(1); clf, box on, hold on ;grid on ; plot(TIME, M(:,1),'r'); if nosig==2 plot(TIME, M(:,2),'b'); end; for k=1:length(ATRTIMED) text(ATRTIMED(k),0,num2str(ANNOTD(k))); end; xlim([TIME(1), TIME(end)]); xlabel('Time / s'); ylabel('Voltage / mV'); string=['ECG signal ',DATAFILE]; title(string); fprintf(1,'\\n$> DISPLAYING DATA FINISHED \n'); % ------------------------------------------------------------------------- fprintf(1,'\\n$> ALL FINISHED \n');
以MIT-BIH数据库中111.dat 为例。
2 去除高频噪声与基线漂移
ECG读取完后,原始ECG信号含有高频噪声和基线漂移,利用小波方法能够去除对应噪声。 详细原理例如以下:将一维的ECG信号进行8层的小波分解后(MATLAB下wavedec函数,小波类型是bior2.6)得到对应的细节系数与近似系数。依据小波原理我们能够知道。1,2层的细节系数包括了大部分高频噪声,8层的近似系数包括了基线漂移。 基于此。我们将1,2层的细节系数(即高频系数置0),8成的近似系数(低频系数)置0。在对应进行小波重构,重构后我们能够明显得到去噪信号。信号无基线漂移。 以下通过图片与代码进一步解说:
小波去噪代码:(matlab)
%%%%%%%%%%%%%%%%%%%去除噪声和基线漂移%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% level=8; wavename='bior2.6'; ecgdata=ECGsignalM1; figure(2); plot(ecgdata(1:points));grid on ;axis tight;axis([1,points,-2,5]); title('原始ECG信号'); %%%%%%%%%%进行小波变换8层 [C,L]=wavedec(ecgdata,level,wavename); %%%%%%%提取尺度系数, A1=appcoef(C,L,wavename,1); A2=appcoef(C,L,wavename,2); A3=appcoef(C,L,wavename,3); A4=appcoef(C,L,wavename,4); A5=appcoef(C,L,wavename,5); A6=appcoef(C,L,wavename,6); A7=appcoef(C,L,wavename,7); A8=appcoef(C,L,wavename,8); %%%%%%%提取细节系数 D1=detcoef(C,L,1); D2=detcoef(C,L,2); D3=detcoef(C,L,3); D4=detcoef(C,L,4); D5=detcoef(C,L,5); D6=detcoef(C,L,6); D7=detcoef(C,L,7); D8=detcoef(C,L,8); %%%%%%%%%%%%重构 A8=zeros(length(A8),1); %去除基线漂移,8层低频信息 RA7=idwt(A8,D8,wavename); RA6=idwt(RA7(1:length(D7)),D7,wavename); RA5=idwt(RA6(1:length(D6)),D6,wavename); RA4=idwt(RA5(1:length(D5)),D5,wavename); RA3=idwt(RA4(1:length(D4)),D4,wavename); RA2=idwt(RA3(1:length(D3)),D3,wavename); D2=zeros(length(D2),1); %去除高频噪声,2层高频噪声 RA1=idwt(RA2(1:length(D2)),D2,wavename); D1=zeros(length(D1),1);%去除高频噪声,1层高频噪声 DenoisingSignal=idwt(RA1,D1,wavename); figure(3); plot(DenoisingSignal); title('去除噪声的ECG信号'); grid on; axis tight;axis([1,points,-2,5]); clear ecgdata;
去噪前后对照图像例如以下:
去噪前:
去噪后:
3 QRS 检測
QRS检測是处理ECG信号的基础,不管最后实现什么样的功能,QRS波的检測都是前提。所以准确的检測QRS波是特征提取的前提。我採用基于二进样条4层小波变换。在3层的细节系数中利用极大极小值方法能够非常好的检測出R波。3层细节系数的选择是基于R波在3层系数下表现的与其它噪声区别最大;详细实现例如以下:
二进样条小波滤波器: 低通滤波器:[1/4 3/4 3/4 1/4]
高通滤波器:[-1/4 -3/4 3/4 1/4]
在第3层细节系数中首先找到极大极小值对:
1)找极大值方法:找出斜率大于0的值,并赋值为1,其余为0,极大值就在序列类似1, 0这种点,即前面一个值比后面的大的值相应的位置点。
2)找极小值方法:类似极大值,找出斜率<0的值相应的位置,并赋值为1。其余的为0,极小值就在类似1,0的序列中相应的位置。即前面一个值比后面的大的值相应的位置点。
检測出的极大极小值例如以下:
3)设置阈值。提取出R波。我们能够看出。R波的值要明显大于其它位置的值,其在3层细节系数的特点也类似于此。 这样我们就能够设置一个可靠的阈值(将全部点分为4部分。求出每部分最大值的平均值T。阈值为T/3)来提取一组相邻的最大最小值对。这样最大最小值间的过0点就是相应于原始信号的R波点。
R波相应的极大极小值对例如以下:
4)补偿R波点。因为在二进样条小波变换的过程中,3层细节系数与原始信号的相应的位置有10个点的漂移。在程序中须要补偿。 (这个在程序中会给出)。
5)找Q S 波。基于R波的位置,在R波位置(在1层细节系数下)的前3个极点为Q波。在R波位置(1细节系数下)的后3个极点为S波。这样我们就将QRS波定位出来。
6)因为不同的情况,可能造成R波的漏检和错检(把T波检測为R波),我们依据相邻R波的距离进行检測漏检与错检。 当相邻R波的距离<0.4 mean(RR)平均距离时,这是错检。这样去除值小的R波。当相邻R波的距离>1.6mean(RR)时。在两个RR波间找到一个最大的极值对,定位R波。这是防止漏检。
经过上述方法,一个鲁棒性非常好的QRS检測方法就出来了。经过測试,QRS检測能达到98%。检測结果R波用红线标注,Q S 波用黑线标注。
4 T P 波检測
P T 波的检測与R波检測有非常大的相同性。仅仅只是 P T 波在4层细节系数中能够表述出更好的特性。相同依据依据极大极小值原理。能够分别检測出T P波,以及他们的起始点与终止点。即TB,TE,PB PE。详细程序我会在稍后的程序中给出。
各波段检測结果例如以下:
详细QRS T P波检查代码例如以下:<pre name="code" class="cpp">level=4; sr=360; %读入ECG信号 %load ecgdata.mat; %load ECGsignalM1.mat; %load Rsignal.mat mydata = DenoisingSignal; ecgdata=mydata'; swa=zeros(4,points);%存储概貌信息 swd=zeros(4,points);%存储细节信息 signal=ecgdata(0*points+1:1*points); %取点信号 %算小波系数和尺度系数 %低通滤波器 1/4 3/4 3/4 1/4 %高通滤波器 -1/4 -3/4 3/4 1/4 %二进样条小波 for i=1:points-3 swa(1,i+3)=1/4*signal(i+3-2^0*0)+3/4*signal(i+3-2^0*1)+3/4*signal(i+3-2^0*2)+1/4*signal(i+3-2^0*3); swd(1,i+3)=-1/4*signal(i+3-2^0*0)-3/4*signal(i+3-2^0*1)+3/4*signal(i+3-2^0*2)+1/4*signal(i+3-2^0*3); end j=2; while j<=level for i=1:points-24 swa(j,i+24)=1/4*swa(j-1,i+24-2^(j-1)*0)+3/4*swa(j-1,i+24-2^(j-1)*1)+3/4*swa(j-1,i+24-2^(j-1)*2)+1/4*swa(j-1,i+24-2^(j-1)*3); swd(j,i+24)=-1/4*swa(j-1,i+24-2^(j-1)*0)-3/4*swa(j-1,i+24-2^(j-1)*1)+3/4*swa(j-1,i+24-2^(j-1)*2)+1/4*swa(j-1,i+24-2^(j-1)*3); end j=j+1; end %画出原信号和尺度系数。小波系数 %figure(10); %subplot(level+1,1,1);plot(ecgdata(1:points));grid on ;axis tight; %title('ECG信号在j=1,2,3,4尺度下的尺度系数对照'); %for i=1:level % subplot(level+1,1,i+1); % plot(swa(i,:));axis tight;grid on; xlabel('time');ylabel(strcat('a ',num2str(i))); %end %figure(11); %subplot(level,1,1); plot(ecgdata(1:points)); grid on;axis tight; %title('ECG信号及其在j=1,2,3,4尺度下的尺度系数及小波系数'); %for i=1:level % subplot(level+1,2,2*(i)+1); % plot(swa(i,:)); axis tight;grid on;xlabel('time'); % ylabel(strcat('a ',num2str(i))); % subplot(level+1,2,2*(i)+2); % plot(swd(i,:)); axis tight;grid on; % ylabel(strcat('d ',num2str(i))); %end %画出原图及小波系数 %figure(12); %subplot(level,1,1); plot(real(ecgdata(1:points)),'b'); grid on;axis tight; %title('ECG信号及其在j=1,2,3,4尺度下的小波系数'); %for i=1:level % subplot(level+1,1,i+1); % plot(swd(i,:),'b'); axis tight;grid on; % ylabel(strcat('d ',num2str(i))); %end %**************************************求正负极大值对**********************% ddw=zeros(size(swd)); pddw=ddw; nddw=ddw; %小波系数的大于0的点 posw=swd.*(swd>0); %斜率大于0 pdw=((posw(:,1:points-1)-posw(:,2:points))<0); %正极大值点 pddw(:,2:points-1)=((pdw(:,1:points-2)-pdw(:,2:points-1))>0); %小波系数小于0的点 negw=swd.*(swd<0); ndw=((negw(:,1:points-1)-negw(:,2:points))>0); %负极大值点 nddw(:,2:points-1)=((ndw(:,1:points-2)-ndw(:,2:points-1))>0); %或运算 ddw=pddw|nddw; ddw(:,1)=1; ddw(:,points)=1; %求出极值点的值,其它点置0 wpeak=ddw.*swd; wpeak(:,1)=wpeak(:,1)+1e-10; wpeak(:,points)=wpeak(:,points)+1e-10; %画出各尺度下极值点 %figure(13); %for i=1:level % subplot(level,1,i); % plot(wpeak(i,:)); axis tight;grid on; %ylabel(strcat('j= ',num2str(i))); %end %subplot(4,1,1); %title('ECG信号在j=1,2,3,4尺度下的小波系数的模极大值点'); interva2=zeros(1,points); intervaqs=zeros(1,points); Mj1=wpeak(1,:); Mj3=wpeak(3,:); Mj4=wpeak(4,:); %画出尺度3极值点 figure(14); plot (Mj3); %title('尺度3下小波系数的模极大值点'); posi=Mj3.*(Mj3>0); %求正极大值的平均 thposi=(max(posi(1:round(points/4)))+max(posi(round(points/4):2*round(points/4)))+max(posi(2*round(points/4):3*round(points/4)))+max(posi(3*round(points/4):4*round(points/4))))/4; posi=(posi>thposi/3); nega=Mj3.*(Mj3<0); %求负极大值的平均 thnega=(min(nega(1:round(points/4)))+min(nega(round(points/4):2*round(points/4)))+min(nega(2*round(points/4):3*round(points/4)))+min(nega(3*round(points/4):4*round(points/4))))/4; nega=-1*(nega<thnega/4); %找出非0点 interva=posi+nega; loca=find(interva); for i=1:length(loca)-1 if abs(loca(i)-loca(i+1))<80 diff(i)=interva(loca(i))-interva(loca(i+1)); else diff(i)=0; end end %找出极值对 loca2=find(diff==-2); %负极大值点 interva2(loca(loca2(1:length(loca2))))=interva(loca(loca2(1:length(loca2)))); %正极大值点 interva2(loca(loca2(1:length(loca2))+1))=interva(loca(loca2(1:length(loca2))+1)); intervaqs(1:points-10)=interva2(11:points); countR=zeros(1,1); countQ=zeros(1,1); countS=zeros(1,1); mark1=0; mark2=0; mark3=0; i=1; j=1; Rnum=0; %*************************求正负极值对过零。即R波峰值,并检測出QRS波起点及终点*******************% while i<points if interva2(i)==-1 mark1=i; i=i+1; while(i<points&interva2(i)==0) i=i+1; end mark2=i; %求极大值对的过零点 mark3= round((abs(Mj3(mark2))*mark1+mark2*abs(Mj3(mark1)))/(abs(Mj3(mark2))+abs(Mj3(mark1)))); %R波极大值点 R_result(j)=mark3-10;%为何-10?经验值吧 countR(mark3-10)=1; %求出QRS波起点 kqs=mark3-10; markq=0; while (kqs>1)&&( markq< 3) if Mj1(kqs)~=0 markq=markq+1; end kqs= kqs -1; end countQ(kqs)=-1; %求出QRS波终点 kqs=mark3-10; marks=0; while (kqs<points)&&( marks<3) if Mj1(kqs)~=0 marks=marks+1; end kqs= kqs+1; end countS(kqs)=-1; i=i+60; j=j+1; Rnum=Rnum+1; end i=i+1; end %************************删除多检点,补偿漏检点**************************% num2=1; while(num2~=0) num2=0; %j=3,过零点 R=find(countR); %过零点间隔 R_R=R(2:length(R))-R(1:length(R)-1); RRmean=mean(R_R); %当两个R波间隔小于0.4RRmean时,去掉值小的R波 for i=2:length(R) if (R(i)-R(i-1))<=0.4*RRmean num2=num2+1; if signal(R(i))>signal(R(i-1)) countR(R(i-1))=0; else countR(R(i))=0; end end end end num1=2; while(num1>0) num1=num1-1; R=find(countR); R_R=R(2:length(R))-R(1:length(R)-1); RRmean=mean(R_R); %当发现R波间隔大于1.6RRmean时,减小阈值,在这一段检測R波 for i=2:length(R) if (R(i)-R(i-1))>1.6*RRmean Mjadjust=wpeak(4,R(i-1)+80:R(i)-80); points2=(R(i)-80)-(R(i-1)+80)+1; %求正极大值点 adjustposi=Mjadjust.*(Mjadjust>0); adjustposi=(adjustposi>thposi/4); %求负极大值点 adjustnega=Mjadjust.*(Mjadjust<0); adjustnega=-1*(adjustnega<thnega/5); %或运算 interva4=adjustposi+adjustnega; %找出非0点 loca3=find(interva4); diff2=interva4(loca3(1:length(loca3)-1))-interva4(loca3(2:length(loca3))); %假设有极大值对,找出极大值对 loca4=find(diff2==-2); interva3=zeros(points2,1)'; for j=1:length(loca4) interva3(loca3(loca4(j)))=interva4(loca3(loca4(j))); interva3(loca3(loca4(j)+1))=interva4(loca3(loca4(j)+1)); end mark4=0; mark5=0; mark6=0; while j<points2 if interva3(j)==-1; mark4=j; j=j+1; while(j<points2&interva3(j)==0) j=j+1; end mark5=j; %求过零点 mark6= round((abs(Mjadjust(mark5))*mark4+mark5*abs(Mjadjust(mark4)))/(abs(Mjadjust(mark5))+abs(Mjadjust(mark4)))); countR(R(i-1)+80+mark6-10)=1; j=j+60; end j=j+1; end end end end %画出原图及标出检測结果 %%%%%%%%%%%%%%%%%%%%%%%%%%開始求PT波段 %对R波点前的波用加窗法。窗体大小为100。然后计算窗体内极大极小的距离 %figure(20); %plot(Mj4); %title('j=4 细节系数'); hold on %%%%%%%还是直接求j=4时的R过零点吧 Mj4posi=Mj4.*(Mj4>0); %求正极大值的平均 Mj4thposi=(max(Mj4posi(1:round(points/4)))+max(Mj4posi(round(points/4):2*round(points/4)))+max(Mj4posi(2*round(points/4):3*round(points/4)))+max(Mj4posi(3*round(points/4):4*round(points/4))))/4; Mj4posi=(Mj4posi>Mj4thposi/3); Mj4nega=Mj4.*(Mj4<0); %求负极大值的平均 Mj4thnega=(min(Mj4nega(1:round(points/4)))+min(Mj4nega(round(points/4):2*round(points/4)))+min(Mj4nega(2*round(points/4):3*round(points/4)))+min(Mj4nega(3*round(points/4):4*round(points/4))))/4; Mj4nega=-1*(Mj4nega<Mj4thnega/4); Mj4interval=Mj4posi+Mj4nega; Mj4local=find(Mj4interval); Mj4interva2=zeros(1,points); for i=1:length(Mj4local)-1 if abs(Mj4local(i)-Mj4local(i+1))<80 Mj4diff(i)=Mj4interval(Mj4local(i))-Mj4interval(Mj4local(i+1)); else Mj4diff(i)=0; end end %找出极值对 Mj4local2=find(Mj4diff==-2); %负极大值点 Mj4interva2(Mj4local(Mj4local2(1:length(Mj4local2))))=Mj4interval(Mj4local(Mj4local2(1:length(Mj4local2)))); %正极大值点 Mj4interva2(Mj4local(Mj4local2(1:length(Mj4local2))+1))=Mj4interval(Mj4local(Mj4local2(1:length(Mj4local2))+1)); mark1=0; mark2=0; mark3=0; Mj4countR=zeros(1,1); Mj4countQ=zeros(1,1); Mj4countS=zeros(1,1); flag=0; while i<points if Mj4interva2(i)==-1 mark1=i; i=i+1; while(i<points&Mj4interva2(i)==0) i=i+1; end mark2=i; %求极大值对的过零点,在R4中极值之间过零点就是R点。 mark3= round((abs(Mj4(mark2))*mark1+mark2*abs(Mj4(mark1)))/(abs(Mj4(mark2))+abs(Mj4(mark1)))); Mj4countR(mark3)=1; Mj4countQ(mark1)=-1; Mj4countS(mark2)=-1; flag=1; end if flag==1 i=i+200; flag=0; else i=i+1; end end %%%%%%%%%%%%%%%%%%%%%%%%找到MJ4的QRS点后,这里缺少对R点的漏点检測和冗余检測。先不去细究了。 %%%%% %%%%%对尺度4下R点检測不够好,须要改进的地方 %%%%%% %figure(200); %plot(Mj4); %title('j=4'); %hold on; %plot(Mj4countR,'r'); %plot(Mj4countQ,'g'); %plot(Mj4countS,'g'); %%%%%%%%%%%%%%%%%%%%%%%%%%Mj4过零点找到%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Rlocated=find(Mj4countR); Qlocated=find(Mj4countQ); Slocated=find(Mj4countS); countPMj4=zeros(1,1); countTMj4=zeros(1,1); countP=zeros(1,1); countPbegin=zeros(1,1); countPend=zeros(1,1); countT=zeros(1,1); countTbegin = zeros(1,1); countTend = zeros(1,1); windowSize=100; %%%%%%%%%%%%%%%%%%%%%%%P波检測%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Rlocated Qlocated 是在尺度4下的坐标 for i=2:length(Rlocated) flag=0; mark4=0; RRinteral=Rlocated(i)-Rlocated(i-1); for j=1:5:(RRinteral*2/3) % windowEnd=Rlocated(i)-30-j; windowEnd=Qlocated(i)-j; windowBegin=windowEnd-windowSize; if windowBegin<Rlocated(i-1)+RRinteral/3 break; end %求窗内的极大极小值 %windowposi=Mj4.*(Mj4>0); %windowthposi=(max(Mj4(windowBegin:windowBegin+windowSize/2))+max(Mj4(windowBegin+windowSize/2+1:windowEnd)))/2; [windowMax,maxindex]=max(Mj4(windowBegin:windowEnd)); [windowMin,minindex]=min(Mj4(windowBegin:windowEnd)); if minindex < maxindex &&((maxindex-minindex)<windowSize*2/3)&&windowMax>0.01&&windowMin<-0.1 flag=1; mark4=round((maxindex+minindex)/2+windowBegin); countPMj4(mark4)=1; countP(mark4-20)=1; countPbegin(windowBegin+minindex-20)=-1; countPend(windowBegin+maxindex-20)=-1; end if flag==1 break; end end if mark4==0&&flag==0 %假设没有P波,在R波左间隔1/3处赋值-1 mark4=round(Rlocated(i)-RRinteral/3); countP(mark4-20)=-1; end end %plot(countPMj4,'g'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%T波检測%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear windowBegin windowEnd maxindex minindex windowMax windowMin mark4 RRinteral; windowSizeQ=100; for i=1:length(Rlocated)-1; flag=0; mark5=0; RRinteral=Rlocated(i+1)-Rlocated(i); for j=1:5:(RRinteral*2/3) % windowBegin=Rlocated(i)+30+j; windowBegin=Slocated(i)+j; windowEnd =windowBegin+windowSizeQ; if windowEnd >Rlocated(i+1)-RRinteral/4 break; end %%%%%求窗体内的极大极小值 [windowMax,maxindex]=max(Mj4(windowBegin:windowEnd)); [windowMin,minindex]=min(Mj4(windowBegin:windowEnd)); if minindex < maxindex &&((maxindex-minindex)<windowSizeQ)&&windowMax>0.1&&windowMin<-0.1 flag=1; mark5=round((maxindex+minindex)/2+windowBegin); countTMj4(mark5)=1; countT(mark5-20)=1;%找到T波峰值点 %%%%%确定T波起始点和终点 countTbegin(windowBegin+minindex-20)=-1; countTend(windowBegin+maxindex-20)=-1; end if flag==1 break; end end if mark5==0 %假设没有T波。在R波右 间隔1/3处赋值-2 mark5=round(Rlocated(i)+ RRinteral/3); countT(mark5)=-2; end end %plot(countTMj4,'g'); %hold off; figure(4); plot(ecgdata(0*points+1:1*points)),grid on,axis tight,axis([1,points,-2,5]); title('ECG信号的各波波段检測'); hold on plot(countR,'r'); plot(countQ,'k'); plot(countS,'k'); for i=1:Rnum if R_result(i)==0; break end plot(R_result(i),ecgdata(R_result(i)),'bo','MarkerSize',10,'MarkerEdgeColor','g'); end plot(countP,'r'); plot(countT,'r'); plot(countPbegin,'k'); plot(countPend,'k'); plot(countTbegin,'k'); plot(countTend,'k'); hold off4特征提取
将各波段的位置提取出来后,我们依据15个距离特征与6个幅值特征作为身份识别的特征。详细信息简下表:
距离特征:
R-Q | R-S | R-P |
P-PB | R-PE | R-T |
R-TB | R-TE | PB-PE |
TB-TE | Q-P | S-T |
P-T | Q-PB | S-TE |
幅值特征:
Q-R | S-R |
PB-P | P-Q |
T-TB | T-S |
我们将MIT-BIH中的101.dat、103.dat、105.dat、106.dat、111.dat分别取出10个这种特征。当中5个作为训练样本、5个作为測试样本。送入神经网络进行训练。
特征提代替码:
%%%%%%%%%%%%%%%%%%%%%%%%%提取特征向量。进行神经网络训练%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%特征向量依据你须要检測部位的不同,选取特征向量。 %%%%%%%%%%%%%%%本例进行身份识别,选取5组信号,即5个同的人,每组数据採取10例ECG信号, %%%%%%%%%%%%%%%提取每例的15个距离特征向量、6个幅值特征向量作为特征数据 %%%%%%%%%%%%%%%距离特征:R-Q R-S R-P R-PBegin R-PEnd R-T R-TBegin R-TEnd %%%%%%%%%%%%%%% PBegin-PEnd TBegin-TEnd Q-P S-T P-T Q-PBegin S-TEnd %%%%%%%%%%%%%%%幅值特征: Q-R S-R PBegin-P P-Q T-TBegin T-S %%%%%%%%%%%%%%每组的10例信号中5个训练5个測试 %%%%%%%%%%%%%%10组信号取第 2 4 6 8 10 12 14 16 18 20个周期, 2 6 10 14 18训练,其余測试 %%%%首先找到R Q S P T峰值。 起点 终点 的位置 locatedR=find(countR); locatedQ=find(countQ); locatedS=find(countS); locatedP=find(countP); locatedPBegin=find(countPbegin); locatedPEnd=find(countPend); locatedTBegin=find(countTbegin); locatedTEnd=find(countTend); locatedT=find(countT); %%%%%%初始化各种特征值 RQ=0;RS=0;RP=0;RPB=0;RPE=0;RT=0;RTB=0;RTE=0; PBPE=0;TBTE=0;QP=0;ST=0;PT=0;QPB=0;STE=0; ampQR=0;ampSR=0;ampPBP=0;ampPQ=0;ampTTB=0;ampTS=0; testECG=zeros(5,21); counttest=1; trainECG=zeros(5,21); counttrain=1; %%%%%%%%%%%%%%%%%開始计算 for i=2:2:20 %距离特征 RQ=abs(locatedR(i)-locatedQ(i)); RS=abs(locatedS(i)-locatedR(i)); RP=abs(locatedR(i)-locatedP(i-1)); RPB=abs(locatedR(i)-locatedPBegin(i-1)); RPE=abs(locatedR(i)-locatedPEnd(i-1)); RT=abs(locatedR(i)-locatedT(i)); RTB=abs(locatedR(i)-locatedTBegin(i)); RTE=abs(locatedR(i)-locatedTEnd(i)); PBPE=abs(locatedPBegin(i-1)-locatedPEnd(i-1)); TBTE=abs(locatedTBegin(i)-locatedTEnd(i)); QP=abs(locatedQ(i)-locatedP(i-1)); ST=abs(locatedS(i)-locatedT(i)); PT=abs(locatedP(i-1)-locatedT(i)); QPB=abs(locatedQ(i)-locatedPBegin(i-1)); STE=abs(locatedS(i)-locatedTEnd(i)); %幅值特征 ampQR=ecgdata(locatedR(i))-ecgdata(locatedQ(i)); ampSR=ecgdata(locatedR(i))-ecgdata(locatedS(i)); ampPBP=ecgdata(locatedP(i-1))-ecgdata(locatedPBegin(i-1)); ampPQ=ecgdata(locatedQ(i))-ecgdata(locatedP(i-1)); ampTTB=ecgdata(locatedT(i))-ecgdata(locatedTBegin(i)); ampTS=ecgdata(locatedT(i))-ecgdata(locatedS(i)); %%%%组成向量,并归一化 featureVector=[RQ,RS,RP,RPB,RPE,RT,RTB,RTE,PBPE,TBTE,QP,ST,PT,QPB,STE]; maxFeature=max(featureVector); minFeature=min(featureVector); for j=1:length(featureVector) featureVector(j)=2*(featureVector(j)-minFeature)/(maxFeature-minFeature)-1; end amplitudeVector=[ampQR,ampSR,ampPBP,ampPQ,ampTTB,ampTS]; maxAmplitude=max(amplitudeVector); minAmplitued=min(amplitudeVector); for j=1:length(amplitudeVector) amplitudeVector(j)=2*(amplitudeVector(j)-minAmplitued)/(maxAmplitude-minAmplitued)-1; end if rem(i,4)==0 testECG(counttest,:)=[featureVector,amplitudeVector]; counttest=counttest+1; else trainECG(counttrain,:)=[featureVector,amplitudeVector]; counttrain=counttrain+1; end clear amplitudeVector featureVector; end save testsample111.mat testECG save trainsample111.mat trainECG
5 BP神经网络训练与检測
我相信非常多人对神经网络比較熟悉了。这里我就不多讲了,在matlab中,主要有三个函数。 newff 负责建立网络, train 负责训练网络, sim 负责进行仿真。调整好參数。就能够进行训练与測试啦。
详细代码例如以下:
clear all; load testsample101.mat; test101=testECG; load testsample103.mat; test103=testECG; load testsample105.mat; test105=testECG; load testsample106.mat; test106=testECG; load testsample111.mat; test111=testECG; load trainsample101.mat; train101=trainECG; load trainsample103.mat; train103=trainECG; load trainsample105.mat; train105=trainECG; load trainsample106.mat; train106=trainECG; load trainsample111.mat; train111=trainECG; %训练样本 train_sample=[ train103' train101' train105' train106' train111']; %21*25 %測试样本 test_sample=[test103' test101' test105' test106' test111']; %输出类别 t=[2 2 2 2 2 1 1 1 1 1 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5]; train_result=ind2vec(t); test_result=ind2vec(t); pr(1:21,1)=-1; pr(1:21,2)=1; net = newff(pr,[21,5],{'tansig' 'purelin'},'traingdx','learngdm'); net.trainParam.epochs=1000; net.trainParam.goal=0.0002; net.trainParam.lr=0.0003; net = train(net,train_sample,train_result); result_sim=sim(net,test_sample); result_sim_ind=vec2ind(result_sim); correct=0; for i=1:length(t) if result_sim_ind(i)==t(i); correct=correct+1; end end disp('正确率:');correct/length(t)
执行结果:正确率为 0.96 左右。效果还不错。
6:本次ECG实现的全部代码与相关原理信息的下载地址(0积分):http://download.csdn.net/detail/yuansanwan123/7530687
希望大家给予批评。有错误之处务必指正。最后感谢能够坚持看到最后的人们!
勉励自己一句话:勤学如春起之苗,不见其长。日有所赠;
辍学如磨刀之石,不见其损,日有所亏。
奋斗吧--碗。
版权声明:本文博客原创文章,博客,未经同意,不得转载。
时间: 2024-11-07 08:52:05