Phng x nhn to v ng dng

SVTH:Nguyn Hong Kim KhnhS dng Support Vector Machine phn tch vi sng trong a k gic ng1GVHD:Th.S L Quc Khi12Bi thuyt trnh gm 4 phn.2I.Tng quan v cu trc vi th gic ng [1,2]3Sau cn au, ri lon gic ng l nhng biu hin thng xuyn nht ca bnh tt. Do vy gic ng c vai tr rt quan trng trong lnh vc y hcMt phng php cn thit nghin cu gic ng lm sng c bn ca con ngi l ghi a k gic ngCc giai on ca gic ng bao gm:Thc tnh W (Wakefulness)Giai on N1 (Non-REM 1)Giai on N2 (Non-REM 2)Giai on N3 (Non-REM 3)Giai on R (REM Rapid Eye Movement) [1]: K. umkov, "Human Sleep and Sleep EEG," MEASUREMENT SCIENCE REVIEW, vol. 4, no. 2, 2004[2]: C. Iber, The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Westchester: American Academy of Sleep Medicine, 2007N bao gm in no (EEG) , in c (EMG) v in mt (EOG). Ni cch khc, a k gic ng l phng php ghi li mt lot cc thng s sinh l ca con ngi trong khi ng. Mt bn ghi a k gic ng c chia thnh nhiu on, mi on c di 30s gi l epoch.

Gic ng bao gm 4 giai on, t trng thi tnh to, n cc giai on su dn ca gic ng. Trong , giai on N2 c c trng bi s xut hin ca cc sng K-complex. Nh vy, vic phn loi cu trc gic ng c quan h mt thit n cc vi th gic ng. Do vy, phn tch nhn bit cc vi sng gic ng l iu cn thit.

3I.Tng quan v cu trc vi th gic ng 4Cc cu trc vi th gm cK-complex phc b KSleep Spindles thoi ngArousal vi thc tnh

K-complex (Phc b K): c m t vi hnh dng c trng, gm 1 sng m ln i trc, theo sau l 1 sng dng. Tng chiu di ca phc b K-complex 0.5s. C nh nhn hn cc nh khc, v khng ta ra pha sau. Bin ln nht, theo sau l 1 SpindlesSleep Spindles (Thoi ng): l cc sng vi tn s t 11-16Hz (thng dng l 12-14Hz), nhn nh v cn i theo 2 pha, xut hin lin tip 12 nh v ko di t 0.5-2s. Bin ca Spindles c khong 30V.

4I.Tng quan v cu trc vi th gic ng 5Arousal

Arousal (Vi thc tnh) l mt trng thi dch chuyn pha t ngt trong EEG, m in hnh l s dch chuyn v cc sng c tn s cao nh alpha, Theta. S xut hin ca Arousal ko di trong t nht 3s.5II.Tng quan v Support Vector Machine6

SVM l mt phng php phn lp d liu

6II.Tng quan v Support Vector Machine [3,4]7Phng php mi trong chui phng php tr tu nhn to (Learning Machine)c pht trin bi V. N. Vapnik v cc cng s c xy dng trn nguyn tc gim thiu cc sai s tng qutc pht trin bi V. N. Vapnik v cc cng s chnh xc cao, ng dng trong khng gian a chiu, tnh linh hot cao[3]: Cesar Seijas, Antonino Caralli, Sergio Villazana, "Estimation of Brain Activity using Support Vectors Machines," in the 3rd International IEEE EMBS Conference on Neural Engineering, Kohala Coast, Hawaii, USA, 2007. [4]: Asa Ben-Hur, Jason Weston, " A Users Guide to Support Vector Machines," in Data Mining Techniques for the Life Sciences, Humana Press, 2010, pp. 223-239 Do vy SVM thng c dng trong vic phn tch cc tn hiu sinh hc7II.Tng quan v Support Vector Machine8Hm dng phn lp trong SVM(1)(2)(3)(4)Hm phn lp s dng trong SVM c th hin nh biu thc 1. Trong ..Vector trng s l hm phi tuyn s dn n s phc tp trong phn tch v yu cu b nh ln. Do vector trng s thng c vit li di dng tng cc thnh phn tuyn tnh.Thay 2 vo 1 ta c 3.EEG thng s dng phn b Gaussian. 8II.Tng quan v Support Vector Machine 9Gi tr ca bin tha iu kin sau:Trn thc t:(5)(6) ti u ha vic phn tch, ngta a ra khi nim ng bin, l khong cch t dim gn nht n ng chia.Khi chuyn i t phi tuyn sang tuyn tnh, chc chn s c s sai st nht nh. gim thiu sai s , ta cn thm 1 h s cng vo nh hm 69III. S dng Support Vector Machine vo phn tch cu trc vi th gic ng [5,6]

10Tn hiu EEG l mt m hnh phc tp v khng gian bao gm nhiu c trngThc t, Support Vector Machine c thc hin mt cch bi bn cho nhng ti nghin cu lin quan n phn tch tn hiu t no.Support Vector Machine c s dng trong EEG bi SVM c ti u ha do d liu c chuyn i vo khng gian khc thng qua h s phi tuyn v phn loi d liu chuyn i [5]: P. BhuvaneswariJ. Satheesh Kumar, "Support Vector Machine Technique for EEG Signals," International Journal of Computer Applications, vol. 63, no. 13, 2013. [6]: Francis E.H. Tay, Lijuan Cao, "Application of support vector machines in financial time series forecasting" Omega-The International Journal of Management Science, vol. 29, no. 4, pp. 309-317, 2011. EEG l mt m hnh phc tp vi nhiu c im. Do vy, vic phn tch EEG gp nhiu kh khn. Phng php tt nht gii quyt vn d l cc phng php Learning Machine, trong c SVM. tng ca SVM l a cc d liu phi tuyn vo vng khng gian tuyn tnh. Nh vy, vic gii quyt cc d liu phi tuyn phc tp a chiu s tr nn d dng hn, linh ng hn.Thc t c nhiu ti nghin cu ng dng ca SVM trong EEG10III. S dng Support Vector Machine vo phn tch cu trc vi th gic ng

11S dng SVM nhn din nh [7]S dng SVM phn tch K-complex [9]S dng SVM phn loi khi u [8][8]: M. M. a. D. Sukanesh, "Towards Detection of Brain Tumor in Electroencephalogram Signals Using Support Vector Machines," International Journal of Computer Theoryand Engineering, vol. 1, no. 5, 2009. [9]: Prof. V.V.Shete, Sachin Elgandelwar, Sapna Sonar, Ashwini Charantimath.Dr.V.D.Mytri., "Detection of K-Complex in Sleep EEG Signal using Support Vector Machine," International Journal of Scientific & Engineering Research, vol. 3, no. 6, 2012.[7]:Giovanni Costantini, Daniele Casali, Massimiliano, Todisco, "An SVM based classification method for EEG signals," in World Scientific and Engineering Academy and Society (WSEAS), USA, 2010.11III. S dng Support Vector Machine vo phn tch cu trc vi th gic ng

12C 2 thng s cn xc nh i vi RBF l [10]:S dng phng php kim tra cho (Cross Validation), ta c c cp thng s[10]: C.-W. Hsu and C.-C. &. L. C.-J. Chang, "A Practical Guide to Support Vector Classification," Department of Computer Science, National Taiwan University ., 2003.Khi s dng SVM trong phn tch tn hiu, ta cn xc nh 2 thng s.Ngi ta nhn thy, C c gi tr 2 m n vi n bng s l t -5 n 15 v gamma mu m tu -15 en 3 s c chnh xc cao.S dng phng php kim tra cho, ta tim c cp thng s (3,-5) c chnh xc cao nht.Kim tra gi tr vng ln cn, cp thng s cui cng c chn l (3.25 v -5.25)12IV. Kt qu v thc hin

13S khng ca chng trnh:1. Chng trnh trn file .m

Trong qu trnh tm hiu SVM trong phn tch vi sng a k gic ng, e xy dng 2 chng trnh. Do la chuong trinh tren file.m v chuong trinh guide.Vi chng trnh trn file.m c chy theo s khi nh trnu tin, ta s l d liu training bao gm ct chn on k-complex v sng nn thng dng. nh dng li cho ph hp vi yeu cau kt qu cui cng tt nht.Tip theo s l d liu test bng cch chnh sa nh dng cho ph hp. Cc kt qu x l c a vo phn loi bng SVM. Qu trnh phn loi gm 2 bc l training data v phn loi d liu cn phn tch.Cui cng x l kt qu ph hp v hin th

13IV. Kt qu v thc hin

14Trong , Support Vector Machine gm 2 qu trnh training d liu v phn lp d liu, s dng cc lnh nh sau:1. Chng trnh trn file .msvmstruct = svmtrain (training, group, name, value)svmclassify (svmstruct, sample, showplot,true)y l code c sn, c lin h vi cc nhm nghin cu khc v chnh sa cho ph hp vi dng d liu s dng14IV. Kt qu v thc hin

15Thc hin chng trnh vi d liu nh sau:1. Chng trnh trn file .mTn: .M.HMS: 325Ngy o: 25/08/2011Thi gian:22:24:19 22:26:48S mu :29800Knh: C4M1V d minh ha trn du lieu tu knh C4M1 ca bnh nhn H, o ngy 25/8/2011. Du lieu co tong cong tat ca 29800 mau15IV. Kt qu v thc hin

161. Chng trnh trn file .mD liu test

D liu th16IV. Kt qu v thc hin

171. Chng trnh trn file .mKt qu:

Sau khi phn tch, kt qu c hin th theo tng figure, moi figure 3000 mu, ng vi 30s.Trong d liu trn, pht hin c 2 sng K-complex nh trn hnh17IV. Kt qu v thc hin

18Giao din ca chng trnh1. Chng trnh trn GUIDE

Text: Tn chng trnhPopupmenu: chn data test.Buttom OKInformation: thng tin ngi bnh Data Option: chn thi gian cn phn tch trong d liu Axes: v d liu trn data.Chng trnh GUIDE vit da tren file. M. Tuy nhin, chng trinh guide co su tuong tac vi ng dng.Nguyen Manh Hung_922 25 0022 25 301819Thanks for your attention19