2. ijece -wavelet based analysis of ecg signal for - reshma - paid

8/20/2019 2. IJECE -Wavelet Based Analysis of ECG Signal for - Reshma - PAID

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WAVELET BASED ANALYSIS OF ECG SIGNAL FOR THE DETECTION OF

MYOCARDIAL INFARCTION USING SVM CLASSIFIER

GOPAKUMAR C1 & RESHMA S2

1Asst. Prof., Department of Electronics, College of Engineering, Karunagapally, Karunagapally, India

2PG Scolar, Department of Electronics, College of Engineering, Karunagapally, Karunagapally, India

ABSTRACT

!is paper introduce a ne" tecni#ue for automatic and accurate detection of eart attac$ "ic is also $no"n as

%yocardial Infarction. !e ECG signals may &e corrupted "it different types of noises. So an efficient noise remo'al

tecni#ue is ine'ita&le. In tis paper, "e use a denoising tecni#ue using "a'elet transform "ic aims to estimate te

signal of interest from te composite signal. (eatures are e)tracted using polynomial fitting algoritm and classification of

ECG &eats is done &y using an S*% +support 'ector macine classifier. !e proposed algoritm is capa&le for automatic

detection of eart attac$ and also impro'es te classification #uality.

KEYWORDS: %yocardial Infarction, -a'elet !ransform, Polynomial (itting, Support *ector %acine

INTRODUCTION

Electrocardiogram +ECG, is a grapical representation grapical representation A typical ECG "a'eform is so"n in

(igure 1 consist of P, /S and ! "a'es. In 20 ours, an ECG can record o'er one la$ eart&eats for a single patient.

Analysis of tese signals manually is a difficult ting for te diagnosis of certain cardiac diseases. So automated analysis of

long term ECG can elp pysicians to understand a patients pysiological state. Automated tools are needed for

cardiologists to analye ECG data accurately. In clinical application, automated classification of long duration ECG signal

is essential. Pre'ious metods to tac$le tis issue "ere super'ised learning approaces3245364, tat adapts some super'ised

metods. In 374, te analysis is &ased on te e)tracted features suc as 8ermite coefficients and neural net"or$ &ased

metods are used for classification. ECG recordings can &e processed &y using tese super'ised learning metods364 &ut

tey do not yield good #uality results. !ese tecni#ues only ta$e te difference &et"een la&els of te entire ECGs &ut not

consider te difference &et"een te la&els of eart &eats "itin te ECG. Semi super'ised 9earning+SS9 strategies30453:4

are introduced to address tis pro&lem. !is approac "ill select a su&set of eart&eats from te training set of ECG data,

manually la&eled &y cardiologists and at last "or$ classifiers on tem.

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A multiple instance learning strategy for ECG classificationIis proposed in 314. !is metod is more comple) for

te analysis of long term ECG data3;4. In tis paper, "e propose an efficient classification system tat impro'es te #uality

of %ultiple instance learning. An efficient "a'elet &ased filtering metods and S*% classification is used. !e proposed

algoritm acie'es good performance on classification of ECGs related to %yocardial Infarction.

!e reminder of tis paper is organied as follo"s. Section II &riefly descri&es a&out myocardial infarction.

Section III descri&es te metodology. Section I* gi'es te simulation results and finally section * presents conclusions

and gi'es directions for future "or$.

MYOCARDIAL INFARCTION

In recent years, %yocardial Infarction+%I is one of te eart diseases causing 'ery ig deat rates. So it is

essential to recognie and locate %I in ECGs. <ne of te ma=or indications of myocardial infarction is te ele'ation in te

S! segment "ic is so"n in (igure 2. <ur main tas$ in tis paper is to detect %I from ECG. %I occurs "en &lood flo"

stops to part of te eart causing damage to te eart muscle.

Figur 2: I.(i#$i'. '0 MI 0r') P$i., ECG

METHODOLOGY

<ur proposed system contains tree main pases> ECG filtering +Preprocessing, (eature e)traction and

classifica5tion. !e metodology used in tis paper is so"n in (igure 6

Figur : B%'# Di$gr$)

ECG Fi%ri.g

!e aim of tis paper is to classify eart&eats. ?efore performing tis tas$, se'eral preprocessing steps "ere

performed on te ECG ra" data. S$in electrodes are used for capturing ECG signal. !ey are prone to contamination &y

'arious types of artifacts or noise. @sually t"o principal sources of ECG signal can &e distinguised. <ne is due to pysical parameters of te recording e#uipment and te second one is te &aseline "ander. ECG is a nonstationary

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&iosignal corrupted &y noise. . It is pro'ed tat -a'elet !ransform34 can &e used as an efficient tool for denoising suc

signals.

!e aim of tis denoising process &ased on "a'elet trans5form is to estimate te signal of interest s+i from te

com5posite signal f+i &y eliminating te noise e+i gi'en tat te composite signal f+i B s+i e+i. !e noisy signal

decom5position is done &y using discrete "a'elet transform+D-! using te Dau&ecies "a'elet. !e o&tained

coefficients are passing troug a tresold314 and setting certain 'alues to ero. !e denoised signal is reco'ered &y

ta$ing te in'ersediscrete "a'elet transform+ID-!. ?y eroing te scaling coefficients of D-!, te &aseline drift also

can &e remo'ed. !is is e#ui'alent to filtering te ECG signal "it a ig pass filter a'ing a cut5off fre#uency of 2.7 8.

F$ur Er$#i'.

%I is usually indicated &y te S! segment of ECG signal. Pea$ detection algoritm3114 is used to detect te

caracteristic points suc as /, S and ! points. After S! segment is e)tracted from te ECG signal, polynomial fitting

algoritm on te S! segment is applied for e)tracting features from it. !e fitted cur'es are similar to te original S!

segments, so te information in te S! segments suc as "idt, sape etc., are e)pressed &y a fe" polynomial coefficients.

@sually n1 coefficients are tere for n order fitting. !e ele'ation in /S pea$ is also an indication for myocardial

infarction. So te eigt of /S pea$ is also included as a feature for te classification.

C%$,,i0i#$i'.

A frame "or$ of classification is so"n in (igure 0. All &eats in te training set is first grouped into different clus5

ters &y using $5means clustering algoritm. !is algoritm utilies te s"arm intelligence algoritm particle s"arm opti5

mier+PS<34 to find a set of optimal 'aria&le "eigts.

Figur : B%'# Di$gr$) 0'r C%$,,i0i#$i'.

Eac cluster is caracteri2ed &y a Gaussian $ernel

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12 G'"$u)$r C & R,@)$ S

+1

"ere

l µ

is te center of te lt cluster, determined &y ta$ing te mean 'alue of eart&eats in te cluster

+2

"ere is te user5defined scale factor. (eature 'ectors are e)tracted &y analying te cluster o'er eart &eats

and finally single instance classifier suc as support 'ector macine classifier +S*%34 is used for classification.

SIMULATION RESULTS

!o illustrate te performance of te proposed system, "e conducted a series of e)periments on te P!? diagnostic

ECG data&ase. (irst in te pre5processing stage, te input ECG signal is filtered &y using a "a'elet &ased filter. Compared

"it con'entional filtering metods, tis "a'elet5&ased filter as ad'antages &ecause of less mean s#uare error 'alue.

Figur <: Origi.$% ECG

!e original ECG signal is so"n in figure . and te filtered ECG signal is so"n in figure :.

In pre'ious "or$, all ECG signals are processed &y a DC! &ased &and pass filter "ose pass &and is set to a

fre#uency inter'al 3f1 f2 4 so as to eliminate te influence of &ot lo"5and ig5fre#uency artifacts.

In tis paper, all te noise and &aseline drift is remo'ed &y using a discrete "a'elet transform &ased filtering

tecni#ue "ic is more relia&le and a'ing less mean s#uare error 'alue compared to tat of DC! &ased &andpass filter.

!e performance comparison of te t"o filtering approaces is so"n in (igure ;. Fe)t step is to detect /, S and ! points

in te filtered ECG. (igure 7 so"s te detection of / and ! points in te filtered ECG signal. A pea$ finding algoritm is

used to detect te caracteristics points in te filtered ECG signal.

S*% classifier is used for classification. A discriminant function f> H / classifies an o&ser'ation ) into oneϵ

of t"o classes, la&eled 1 or 51. !e e#uations for Sensiti'ity, Specificity and Accuracy are so"n &elo".

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Figur =: Fi%&r( ECG

Figur 3: Pr0'r)$.# C')"$ri,'.

Specificity B

! F

! F ( P

+6

Specificity B

! F

! F ( P

+0

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1 G'"$u)$r C & R,@)$ S

Figur 4: D#i'. '0 R; S $.( T P'i.,

Accuracy B! P ! F

P F +

"ere !P, !F, (P, (F, P and F are true positi'e, true negati'e, false positi'e, false negati'e, positi'e and negati'e

respecti'ely. Compared to oter classifiers suc as $nn+$5nearest neig&or classifier, S*% classifier yields &etter results

in te classification of ECG signals and o&tained a classification accuracy of 1 .

T$% 1: Pr0'r)$.# C')"$ri,'. '0 KNN $.( SVM C%$,,i0ir

S.,i&i?i&! S"#i0i#i&! A##ur$#!

KFF 7 I 7D I 70 I

S*% A1 I 77 I A1 I

(igure so"s te performance comparison of KFF and S*% classifiers plotted &y ta$ing te sensiti'ity and

specificity 'alues for different scale factors + . Sensiti'ity and specificity are iger for large scale factors.

CONCLUSIONS

In tis paper, "e proposed an automated classification of ECG &eats using S*% classifier. !e proposed system

accomplises preprocessing effecti'ely &y using discrete "a'elet transform, "ic is an efficient tool for denoising

&iological signals. A set of features are e)ploited to caracterie eac &eat. !e proposed system sol'es te pro&lem of

automatic detection of %I from patients ECG. !is paper consider &ot intra and inter ECG differences for &etter

classification.

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W$?% B$,( A.$%!,i, '0 ECG Sig.$% 0'r @ D#i'. '0 M!'#$r(i$% I.0$r#i'. U,i.g SVM C%$,,i0ir 1<

Figur 6: Pr0'r)$.# C')"$ri,'.

-en tested on real ECG datasets from te P!? diagnostic data&ase, our metod acie'es &etter performance

tan pre'ious algoritms. !e proposed system is 'ery muc useful in clinical application for te detection of myocardial

infarction. <f course, tere is a scope for impro'ement. !at is furter tuning of te parameters "ill yield &etter results.

ACKNOWLEDGEMENTS

I "ould li$e to ta$e tis opportunity to e)press my sincere gratitude to all tose "o a'e guided in te successful

completion of tis paper.

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2. ijece -wavelet based analysis of ecg signal for - reshma - paid

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