基于獨立分量分析特征提取的心電信號模式識別的研究

1、HermiteICA PCA WTMIT-BIHMIT-BIH 13ICAICASVM-96.77%46-ICA96.87%-ICA98.23%-ICA98.65%ICAICAIIMIT-BIH IIIAutomatic Patten Recognition of ECG Signals Based onIndependent Component Analysis Feature ExtractionABSTRACTCardiovascular disease is the leading cause of death around world.Electrocardiogram (ECGsu

2、pervising is the most important and efcient way of preventing heart attack.The types of ECG arrhythmia is an important standard to measure the variation of cardiovascular activity.Recognizing arrhythmia on its early stage is very important for diagnosing and fore-casting the state of illness,and it

3、is also critical for the further treatment of the patients.Those former research on recognizing heart beat arrhythmia mainly used the ECG morphology and heartbeat interval,wavelet transform or Hermit representation as the feature extraction meth-ods and achieved certain results.This thesis proposes

4、a new feature extraction method based on Independent Component Analysis(ICAto classify the heartbeat arrhythmia.The method is used to recognize normal beats and the other13types of arrhythmia heart beats from the data provided by MIT-BIH arrhythmia database.ICA is a signal processing technique to ex

5、tract independent components from muti-dimensional mixed signals.It has been widely used in manyelds,such as biomedical signal processing,speech recognition,and antenna array processing.This thesis proposes two architectures for feature extraction based on independent component analysis:one is to le

6、arn the independent feature basis functions of ECG heartbeat signals,and the other is to learn the independent feature representation of ECG heartbeat signals.From computer experiments,it is demonstrated that both two architectures have their advantages and both are effective for the heartbeat recog

7、nition.In addition,an overcomplete feature set which is constructed by different types of fea-ture extraction methods is proposed in the thesis.By combining the characteristics of differ-ent types of features,the overcomplete features makes those different features work together and obtains a better

8、 feature representation of the ECG signals.In this thesis,an overcom-plete feature extraction method combining ICA basis function s coefcients and wavelet transform coefcients is propose for ECG recognition.Because there are some features thatare irrelevant to the classication or have redundant info

9、rmation in the overcomplete feature set,a feature selection based on mutual information is applied on the overcomplete features, and we obtain the relevant features for ECG recognition.We choose Support Vector Machine(SVMas the classier to accomplish the clas-sication.SVM is a pattern classication m

10、ethod based on Structural Risk Minimization principle.Because of its strong learning ability and generalization ability SVM has been applied to many areas.In our experiments,we use the training samples from normal ECG signals to learn the basis functions of the feature space,and all types of arrhyth

11、mia signals in to the space to extract features.Then we use these features to train a SVM with Gaussian Ra-dial Basis Function(Gaussian-RBFkernel,and obtain an integrated ECG signal recognition system.Each kind of feature extraction methods proposed in the thesis are tested in experiments and compar

12、ed:-If the wavelet features are used only,the average recognition accuracy is96.77%.-By the feature extraction architecture which use the ICA method to learn the sta-tistical independent basis functions,we obtain an average classication accuracyto96.87%.-By the feature extraction architecture which

13、use the ICA method to learn the sta-tistical independent features,the average accuracy for classication is98.23%-the average recognition accuracy improves to98.65%,when the recognition sys-tem takes the overcomplete features which constructed the ICA features and thewavelet features.and we also comp

14、are our method to the other methods proposed in recent years.Finally, computer simulations show that the overcomplete feature extraction based on ICA method could present the differences between each type of ECG arrhythmia signals better and could improve the ECG arrhythmia recognition.KEY WORDS:Ind

15、ependent Component Analysis,ECG,arrhythmia,Principal Compo-nent Analysis,Wavelet Transform,Overcomplete Feature,Mutual Information,MIT-BIH arrhythmia databaseVICA Independent Component AnalysisECG electrocardiographyWT Wavelet TransformPCA Principal Component AnalysisEMG electromyographyEEG electroe

16、ncephalographyMEG magnetoencephalographyHMM Hidden Markov ModelANN Artical Neural NetworkBSP Blind Signal ProcessingMIMO Multiple-Input Multiple-OutputBSS Blind Source SeparationBSE Blind Signal ExtractionMBD Multiple Blind DeconvolutionSVM Support Vector MachineRBF Radial Basis FunctionAPC Atrial P

17、remature ContractionPVC Premature Ventricular ContractionLBBB Left Bundle Branch Block BeatRBBB Right bundle Branch Block BeatPACE Paced beatAPB Atrial premature beatAAPB Aberrated atrial premature beat VF Ventricularutter waveFusion Fusion of ventricular and normal beatBAPB Block atrial premature b

18、eatJEB Nodal(junctionalescape beatFP Fusion of paced and normal beatVE Ventricular escape beatJPB Nodal(junctionalpremature beatCR Compression RatioPRD Percent root-mean-square differenceIX200712920071292007129Anais NinpCO21.1,(ECG0.04-0.07sPQRS TECG 1.1P0.11P-R60-90/0.12-0.20 1.1Fig1.1A standard EC

19、G heartbeat signalQRS QRSQ Q R RS QRSQRS Q R S QRS0.06-0.11S-T QRS TTQ-T QRS T0.32-0.44ECGECG ECGECGECG50ECGECGECG2ECG ECG ECG ECG Holter ECGECG 70ECG1.22020601( 1.24: (1.2 3( K-L(SOMKohonen2 (ICA,Independent Component Analysis (PCA,Principle Component Analysis Wavelet Analysis1.361ECGECG2ICA ICA3IC

20、A ICA4ICA5ICA6 5ICA2.1ICABSP-MIMOx (t =x 1(t , x 2(t , ···, x m (t Ts (t =s 1(t , s 2(t , ···, s n (t T 2.13y (t =y 1(t , y 2(t , ···, y n (t T42.2ICABSSBSEMBD3 . . . . . . . .s 1s 2s nv 1v 2v ky 1y 2y mx 1x + 2.1Fig 2.1General model of Blind Signal Proce

21、ssing (BSPProblem (b2.26Fig 2.2Linear Mixture Modelscaling permutationdelay52.2ICAICA ICA2.2.1PCAPCA 7PCAPCA xKarhunen-Loe ve PCAR xx=Ex(kx T(k=VV T(2.1=diag1,2,···,m(1>2>···>mmV=v1,v2,···,v mKarhunen-Loe ve xy p=V T S x(2.2y p=y1(k,y2(k,·&#

22、183;·,y n(kT PCs V S= v1,v2,···,v nT v i=v i1,v i2,···,v imT(i=1,2,···,n2.2.2Whiteningprewhitening sphering yR yy=Eyy T=I white x whitening Wy(k=Wx(k(2.3W=U1/2V T(2.4UV 2.12.2.3ICA2.2(a-x(k=As(k+v(k(2.5ICAA m×n v(k=v1(k,v2(k,···,v

23、 m(kT ICA71.mn2.3.4.1.H2.ICAp.d.f.p(sp i(s i3.4.ICA W x W yy(k=Wx(k=WAs(k(2.6 y W AWA=PD(2.7P DPCA ICAICAPCAPCA R yy=R xx R yy=IICAICA ICAICAICAon-line learning batch modelHWsxyg(tz2.36Fig 2.3Architecture of Infomax AlgorithmICA82.2.41988Linsker 9InfomaxNadalParga 10BellSejnowski 112.3g (·H (z

24、H (z =H (z 1+H (z 2+···+H (z n I (z (2.8I (z H (z i H (z i =Elog p (z i (2.9p (z i =p (y i |z i y i|=p (y i |g i (y i |(2.10(2.9H (z i =E logp (y i |gi (y i |(2.11(2.11(2.8H (z i =E logp (y 1|g 1(y 1|+···E log p (y m |g m (y m|I (z ICA=ni=1Elogp(y i|g i(y i|I(z(2.12H(zW

25、 =W(I(zWni=1Elogp(y i|g i(y i|(2.13 H(zI(zp(y i=|g i(y i|W ni=1Elog p(y i|g i(y i|I(z2.8H(zI(zni=1H(z i2.10p(y i=|g i(y i|H(zg i(y iWH(z=H(g(y=H(g(Wx=H(x+log|det(W|+ni=1Elog g i(y i(2.14WH(zW =H(xW+log|det(W|W+Wni=1Elog gi(y i(2.15H(xW0log|det(W|W=(W T1=WT(2.16Wni=1Elog gi(y i=(yx T(2.17(y12score fu

26、nction(y=g 1(y1g 1(y1,···,gn(y ngn(y nT(2.182.15H(zW=WT(yx T(2.19W=(WT(yx T(2.20 2.20InfomaxAmari13H(zWW T WWH(zWW T W.(2.21 W=(WT(yx TW T W=(I(yy TW(2.22I 2.2214W 2.20g i(y ilogistic y3tanh(yICA5510.90.80.70.60.50.40.30.20.10(alogistic function543210123451501005050100150(bCubic funct

27、ion5432101234510.80.60.40.200.20.40.60.81(ctanh function2.4Fig 2.4Frequently used nonlinear functions2.2.51997Girolami15p (y p G (y KL16J (y =D(p (y |p G (y =p (y log p (y p G (y d y (2.23p G (y p (y yWy =Wxyp (y yn i =1J (y i =ni =1D(p (y i |p G (y i =p (y log ni =1p (y i n i =1p G (y id y=p(ylogni

28、=1p(y ip G(yd y=p(ylogni=1p(y ip(yd y+p(ylogp(yp G(yd y=D(ni=1p(y i|p(y=I(y+J(y(2.24 2.23ni=1J(y i=I(yH(yp(ylog p G(yd y=I(yH(x12log(2en det yy T (2.25y yni=1j(y i=I(yH(x12log(2en(2.26Wni=1J(y i=W(I(yH(x12log(2en(2.27Wni=1J(y i=W(I(y(2.282.2.6x=As xp(x=|det W|p(s=|det W|ni=1p(s i(2.29ICA W=(w1,·

29、;··,w nT=A1,s i=w T i xp(x=|det W|ni=1p(w T i x(2.30T x(1,x(2,···,x(TL(W=Tt=1|det W|ni=1p(w Tix(2.31log L(W=Tt=1ni=1log p(w T i x(t+T log|det W|(2.321 T log L(W=Eni=1log p(w Tix+log|det W|(2.33(2.34W1 T log LW=WT+Eg(Wxx T(2.35g(y=(g1(y1,···,g n(y ng i(·W=

30、(WT+g(yx(2.36 Bell-Sejnowski11y=Wx2.2.77J(y i11223,i+14824,i+74843,i+1823,i4,i(2.37n,i y i n3,i=m3,i(2.384,i=m4,i3m22,i(2.39 m n,i y i n m n,i=Ey n ini=1J(y i=148i=1n24,i(2.401994Comon7L(W=ni=124,i(2.41w isign(4,ixy3iw iw i/ w i (2.42(2.43 2.2.8PCAPCAL(W=E xni=1(w Tixw i 22(2.44L(W=E xni=1g i(w Tixw

31、 i 22(2.45g i(·n i=1n17LMSER y i=w T i xICANLPCAx W W T W= WW T=I(3.47L(W=E xW T g(y 22(2.46 g(y=g1(y1,.,g n(y nTxW T g(y 22= yWW T g(y 22= yg(y 22=ni=1y ig i(y i2(2.47L(W=ni=1Ey ig i(y i2(2.482.3ICAICAICA18,1920ICAECGFECGECGEEG/MEG212.3.1ICAECG50Hz(EMG,.P QRS QRS P TICA ICA22,232.3.2ICA24,252.

32、3.3ICABSS(ICA2628P QRS TICA18ICA3.129x I(xI(xa i(xI(x=ni=1a i(xs i,(3.1s i I(xs ix=(x1,x2,.,x mT,x=As(3.2 ICAFourier3.1.13.13.1Fig3.1Nonsparse representation of signalPCA30PCAX=x1,x2,.,x MMS i=E(x(xT,xX(3.3 PCA P=p1,p2,.,p n na=P T(x i.(3.4 nPCA20ICAPCA(1PCAPCA(2PCA(3E(xT p i(xT p j=0,i=j.(3.5(4PCAI

33、=AA=T I PCA/3.1.2PCA3.2Field31PCA3.3PCA 3.3(aPCA3.3(b(ICAPCA ICA213.2Fig 3.2Sparse representation of signalxyPCA(axy(ICA(b3.3PCAFig 3.3The distributions of PCA basis functions and ICA basis functions under the non-Gaussiandistribution data22ICAIx 1 x 2x ni i SA 11A 12A 1n3.4ICA IFig 3.4The architect

34、ureI of ICA feature extraction(1ICA (2ICA W(3ICAPCA(4ICA3.2ICA3.2.1ICAICAXmnICAXICA1.ICAXm ×nXnm ijjiICA 3.42.X1m ×nXmIIx 1x 2x mi i SA 11A 12A 1m 3.5ICA IIFig 3.5The architectureII of ICA feature extractionni jijICA3.53.2.2ICAIICAx T =M i =1a i s T i ,(3.6xxa 1s T 1+a 2s T 2+,··

35、·,+a m s Tms T 1,s T 2,.,s Tma 1,a 2,···,a mxs T 1,s T 2,.,s Tm3.6XICASSs T 1,s T 2,.,s TmXICAS WX test B test=X testSSS ICA10000256ICA256256X10000×256ICAX m×256,m<256X m×256ICA mPrincipal Component Analysis,PCAm PCA ICA X m×256X m P m R m=XP m XX=Rm P T m

36、P m ICAW m P T m=S m,P T m=W1m S m(3.7X=Rm P T m=R m W1m S m(3.8 S m m mB=R m W1m(3.9B test=X test P m W1m(3.10PCA18PCA181898%1898% 3.6I3.2.3ICA IIICAx=Ni=1a i s i(3.11100200-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-5051001002

37、00-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-505100100200-10-505103.6ICA IFig 3.6The basis functions of normal heartbeat learned by the ICA feature extraction architectureI =0.2·+(0.8·+0.1·+·

38、··3.7ICA 0.2·a i +(0.8·a j +0.1·a k +.Fig 3.7A normal heartbeat is a linear combination of ICA bases with certain coefcients,i.e.0.2·a i +(0.8·a j +0.1·a k +.xxa 1s 1+a 2s 2+···+a N s Ns 1,s 2,.,s Na 1,a 2,.,a Nxa 1,a 2,.,a Ns 1,s 2,.,s Nxa 1,a

39、2,.,a NXXICAAAa 1,a 2,.,a N3.7ICA overlapping00Kurtosis=Ex4Ex23(3.1232,33I IIIII XAX test S test=WX test W=A1256ICA X PCAP m=V m X m ICA P mP m=A m S m,A1m P m=S m(3.13A m m m S mS test=A1m V m X test(3.14I PCA18 3.8II18100200-0.0500.050.10.150100200-0.2-0.100.10.20100200-0.200.20.40.60100200-0.0500

40、.050.10.150100200-0.0500.050.10.150100200-0.1-0.0500.050.10100200-0.1-0.0500.050.10100200-0.100.10.20.30100200-0.200.20.40.60100200-0.100.10.20.30100200-0.2-0.100.10.20100200-0.2-0.100.10.20100200-0.100.10.20.30100200-0.2-0.100.10.20100200-0.100.10.20.30100200-0.4-0.200.20.40100200-0.100.10.20.30100

41、200-0.100.10.20.3(a10551005001000105510050010001500200010551002004006008001050510050010001050510050010001500105510020040060010551002004006008001050510020040060080010551002004006008001055100200400600800105051002004006001050510020040060010551002004006008001055100500100010505100200400600105051002004006

42、0080010551002004006001055100100200300400(b3.8ICA IIFig 3.8The basis functions of normal heartbeat learned by the ICA feature extraction architectureII284.14.1.1ICAICAICAT=I+W.(4.1I ICA W:=1:14.1.234(x(xl(x/ss,l=2s/2(2s xl(4.2fD1A1D2A2D3A3D4A44.1Fig4.1Decomposition of Discrete Wavelet TransformW f(s,

43、l=1s+f(xxsld x=f¯s(l(4.3¯s(t=1s(ts(t(ts l35=/360.5Hz40Hz 374Approximation A4 4.112Details304.14Table reftab:frequencyBand Frequent bands of4scale wavelet package decompositionAAAA40-11.25HzDAAA411.25-22.5HzADAA422.5-33.75HzDDAA433.75-45HzDD245-90HzD190-180Hz4.2 100256304.21.38402.315010015

44、02002500.50.511.5256 DimensionsV o l t a g e (m v (a5101520253021012330 DimensionsR a n g e(b4.2(a100256(b100db8Fig 4.2Wavelet features of heartbeat signals.(a100normal heartbeat signal in 256dimensions.(bThe corresponding wavelet features of the 100heartbeat signals.Using ”db8”wavelet and extractin

45、gthe A4features.32 4.343Fig 4.3Relations between the input features and the classes4.2.141x i cA B A B x ic (A =c (B x i4.2.2Mutual Information4.3f i f s CF C I (C ; F H (C ; F =H (C +H (F I (C ; F (4.4 33H (C ; F C F H (C H (F C FI (C ; F =I (F ; C = cP (c, f logP (c, f P (c P (f d f (4.5c fn F kS

46、F H (C |S I (C ; S 42(1F n S (2(MIf F I (C ; f (3I (C ; f f f F S f F, max I (C ; f ; F F f ; S f .(4(aF f I (f, S ; C (bF I (f, S ; C f iS max I (f i , S ; C ; F Ff , S f S.(5S4.4234I (C ; f i , f s f i 4.4,4I (C ; F Battiti MIFS 42I (f, S ; C I (C ; f I (f ; f f f 4 34 selection(4(aI (f ; s (bI (C

47、 ; f s SI (f ; s fS=0MIFSI (C ; f i I(f i ; f s f i=1MIFS4.53-1f s4.51MIFSMIFS434I (C ; f i , f s I (C ; f i , f s =I (C ; f s +I (C ; f i |f s .(4.6I (C ; f s 4.424I (C ; f i |f s f s 35 f i I (C ; f i |f s I (C ; f i |f s =I (C ; f i I (f s ; f i I (f s ; f i |C . (4.7I (f s ; f i I (f s ; f i |C 4.44I (f s ; f i 14H (f s |C H (f s =I (f s ; f i |C I (f s ; f i , (4.8I (f s ; f i |C I (f s ; f i |C = H (f s |C H (f s I (f s ; f i (4.9I (f i ; C |f s =I (f i ; C (1 H (f