English Elektrik Fi Jour

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American Journal of Networks and Communications2013; 2(1) : 9-16Published online February 20, 2013 (http://!s"ien"epublishin##roup!"o$/%/a%n")doi: 10!116&'/%!a%n"!20130201!12

A journal of real peak recognition of electrocardiogram

(ecg) signals using neural networkTarmizi Amani Izzah

1, Syed Sahal Nazli Alhady

1, Umi Kalthum Ngah, Wan Pauzi Ibrahim

2

1"hool o *le"tri"al *le"troni"s *n#ineerin#, +niersiti ains alaysia (*n#ineerin# .a$pus), ibon# ebal, P, Penan#2"hool o edi"al "ien"es, +niersiti ains alaysia (ealth .a$pus), 1610, uban# erian, elantan

Email address:i44ah!a$ani5y$ail!"o$ (! 7! ar$i4i), sahal5en#!us$!$y (! ! ! 7lhady), eeu$i5en#!us$!$y (+! ! #ah),

pau4i58b!us$!$y (! P! brahi$)

T !ite this arti!le:ar$i4i 7$ani 44ah, yed ahal a4li 7lhady, +$i althu$ #ah, an Pau4i brahi$! 7 ournal o eal Pea8 e"o#nition o *le"-

tro"ardio#ra$ (*.<) i#nals +sin# eural etor8! American Journal of Networks and Communications, =ol! 2, o! 1, 2013, pp! 9-16!doi: 10!116&'/%!a%n"!20130201!12

Abstra!t: his paper des"ribes about the analysis o ele"tro"ardio#ra$ (*.<) si#nals usin# neural netor8 approa"h!

eart stru"ture is a uni>ue syste$ that "an #enerate *.< si#nals independently ia heart "ontra"tion! ?asi"ally, an *.<

si#nal "onsists o P@ ae! 7ll these aes are represented respe"tie heart un"tions! or$al healthy heart "an be

si$ply re"o#ni4ed by nor$al *.< si#nal hile heart disorder or arrhyth$ias si#nals "ontain dieren"es in ter$s o eatures

and $orpholo#i"al attributes in their "orrespondin# *.< aeor$! o$e $a%or i$portant eatures ill be eAtra"ted ro$

*.< si#nals su"h as a$plitude, duration, pre-#radient, post-#radient and so on! hese eatures ill then be ed as an input to

neural netor8 syste$! he tar#et output represented real pea8s o the si#nals is also bein# deined usin# a binary nu$ber!

esult obtained shoin# that neural netor8 pattern re"o#nition is able to "lassiy and re"o#ni4e the real pea8s a""ordin#ly

ith oerall a""ura"y o '1!6B althou#h there $i#ht be li$itations and $is"lassii"ation happened! Future re"o$$endations

hae been hi#hli#hted to i$proe netor8Cs peror$an"e in order to #et better and $ore a""urate result!

Key"rds: eart, *.< i#nal, Features *Atra"tion, eural etor8 7nd atlab i$ulation

1# State$%$the Art

eural netor8 noadays has been applied eAtensiely in

ide areas in"ludin# "lassii"ation, dete"tion, aerospa"e,

ore"astin#, heart dia#nosis and $any $ore! his pro%e"t

applied neural netor8 $ethod in analysin# "ardia" rhyth$s!

e"o#nition o real pea8s o *.< si#nals is i$portant to

dia#nose the heart diseases! Do"tors obtained *.< dataro$ olter dei"e that re"orded patientCs heart beat and

they peror$ analysis $anually based on the aeor$

"hara"teristi"s! .urrently, "ardiolo#ist or do"tors identiy

the real pea8 based on their 8noled#e and preious eApe-

rien"es! o$e *.< si#nals easily obtained the real pea8 by

loo8in# at the aeor$ pattern but there is also so$e si#-

nals hi"h is ery dii"ult to identiy their real pea8! his

$anual identii"ation $i#ht "ontain ina""ura"y and s$all

per"enta#e o error! n addition, it "ould be a tedious ay

espe"ially hen analy4in# a ery lo re>uen"y o *.<

si#nals! Do"tors nor$ally need an ade>uate ti$e to study

and eriy the *.< aeor$ beore #ettin# the "orre"tresult or eery patient eAa$ined! his or8s done dei-

nitely ti$e-"onsu$in# and not the ei"ient ay!

hus, this idea "o$es up that "ould be benei"ial to "ar-

diolo#ists to re"o#ni4e the real pea8 usin# ne approa"h

hi"h is neural netor8! eural netor8 has the ability to

$e$ori4e the pattern and dire"tly #ies the result a""or-

din#ly! his eases the do"tors and analysis "ould be done in

ay that is $ore ei"ient! eural netor8 also eAhibits

independent behaior as ell as sel-learnin#! t desi#ned insu"h a ay that eAposed to enou#h trainin# until it "o$es to

a #enerali4ation state! <enerali4ation $eans the netor8 "an

$e$ori4e the data pattern and able to #ie result "orre"tly

based on the preious learnin# #ien! he syste$ then tested

to see its a""ura"y and peror$an"e!

t is then be"o$in# a #ood $o$entu$ to eAhort i$-

proe$ent in ele"tro"ardio#raphy by oerin# a reliable as

ell as "o$prehensie solution or better *.< dia#nosis

E1&! ?esides, by usin# neural netor8 $ethod, thin#s ill

be $u"h si$pler, ti$e-sain#s as ell as redu"in# the needs

o hu$an eorts as $a"hine has been trained to peror$ the

desired or8load!



10 ar$i4i !7 et al.: 7 %ournal o real pea8 re"o#nition o ele"tro"ardio#ra$ (e"#) si#nals usin# neural netor8

2# Intrdu!tin

he needs o te"hnolo#y and "o$puteri4ed analysis usa#e

has eAhorted resear"hers, proessionals, en#ineers and other

eApert people "o$binin# their eorts to#ether in i$ple-

$entin# >uality dia#nosis tools! he ter$ >uality has been

interpreted as easier and aster analysis, la"8 $aintenan"e,hi#h ei"ient as ell as lo in the "ost! Due to that, another

approa"h to analyse *.< si#nals has been "hosen by usin#

neural netor8 $ethod ia $atlab sotare, as $atlab is

ell 8non ith $ultiun"tion and poerul "o$puteri4ed

tool sotare! his pro%e"t has applied neural netor8 ap-

proa"h to analy4e *.< si#nals o"usin# on real pea8s re"-

o#nition sin"e it proides aluable inor$ation to do"tors

re#ardin# heart dia#nosis! e"o#nition o real pea8 "orre"tly

is absolutely essential as it indi"ated the "ondition o heart as

ell as rele"ts to its un"tionality!

2.1. Generation of ECG Signal

he "orrespondin# part in the heart plays their respe"tie

roles! inoatrial (7) node ill eA"ite the beats that "aused

heart $us"les to "ontra"t! ?elo is shon the lo"ation o

7trioentri"ular (7=) node and 7 node hi"h are respon-

sible or #eneratin# *.< si#nal in the hu$anCs heart!

Figure 1. Location of SA and AV node [1].

he "ontra"tion o heartCs $us"les soon ill be re"orded

as an ele"tri"al a"tiity o the heart "alled *.< si#nal! ?ased

on the pattern o *.< re"ordin#, heart status "ould be iden-

tiied hether possessed o any "ardia" arrhyth$ias or oth-

erise! 7s 8non, heart $us"le possessed the "hara"teristi"

o depolari4ation and repolari4ation! Depolari4ation is re-

erred to the ele"tri"al potential a"tiity eA"ited by heart

$us"les hile repolari4ation is a relaAation state hen the

heart "han#in# ba"8 to its ori#inal position! P ae #ener-

ated due to the atrial depolari4ation, @ "o$pleA

represented entri"ular depolari4ation hile ae

represented entri"ular repolari4ation E2! Fi#ure belo

shoed the "orrespondin# part o heart un"tion ith respe"t

to the *.< si#nal obtained!

7bnor$alities happened in the respe"tie aes se#$ent

ill proide ideas to do"tors and "ardiolo#ists at here the

part o heart is hain# proble$s!

Figure 2. ECG sinal !ased on "eart function [#], [$].

2.2. Neural Network

7 neural netor8 is a type o "o$putational $odel hi"h

is able to sole $ulti proble$s in arious ields! t pro"esses

the inor$ation in a si$ilar ay as the hu$an brain "on"ept

pro"essin# the inor$ation E! ?asi"ally, neural netor8

"onsists o lar#e pro"essin# ele$ents "alled neurons or8-

in# to#ether to peror$ spe"ii" tas8s! 7s in the hu$an brain,

there are thousands o dendrites hi"h "ontain inor$ation

si#nals! hey trans$itted the si#nals to the aAon in the or$

o ele"tri"al spi8es! he aAon then sends the si#nals to

another dendrites "ausin# to a synapse! his synapse o"-

"urred hen eA"itatory input is sui"iently lar#e than the

inhibitory input, and this "on"ept o si#nal trans$ission also

depi"ted on ho neural netor8 pro"ess inputs re"eied!

Fi#ure belo shon dendrites related stru"tures or "learer

understandin#!

Figure 3. %endrites [&].

Figure 4. S'na(ses [&].



7$eri"an ournal o etor8s and .o$$uni"ations 2013, 2(1): 9-16 11

n neural netor8, dendrites "arryin# si#nals "an be

analo#y as $ultiple inputs "olle"ted to#ether or su$$ation!

hen, the "o$bined inputs ill be a"tiated by a"tiation

un"tion! nputs that eA"eed threshold alue ill be eed to

the output layer or inal pro"essin#! Durin# pro"essin#

sta#e, inputs ill be trained to produ"e desired tar#et outputs

until it "o$e to a #enerali4ation sta#e! <enerali4ation $eans

at a "ondition here the netor8 is able to re"o#ni4e the

inputs and the "orrespondin# tar#ets ater under#one the

trainin# #ien! hen, the netor8 ill be tested by #ien

ne inputs si#nal to ealuate its peror$an"e and to see ho

a""urate the output produ"ed ill be "o$parin# to the tar#et!

Fi#ure belo depi"ted the analo#y o hu$an brain "on"ept

pro"ess the inor$ation to the neural netor8 syste$!

Figure 5. Neuron model [&].

Figure 6. Neural networks [&].

eural netor8 "onsists o seeral ar"hite"tures, ro$

si$ple stru"ture until the "o$pli"ated ones!

2.2.1. Single-Layer Feed forward Network

his is the si$plest or$ o netor8 ar"hite"ture ithonly sin#le layer output ithout any hidden layer! 7n input

layer o sour"e nodes ill dire"tly pro%e"ts onto the output

layer o neurons or "o$putation nodes, %ust in one ay

butnot i"e ersa! in#le layer is reerrin# to the output layer

hi"h is %ust sin#le output and not "onsidered the input layer

o sour"e nodes sin"e no "o$putation peror$ed there EG!

Fi#ure G shoed the "orrespondin# i#ure in"ludin# label!

Figure !. Sinle la'er feedforward networks [)].

2.1.2. "ultilayer Feed-forward Network

his se"ond layer is dier ro$ aboe sin"e it has one or

$ore hidden layers! he "o$putation also ta8es pla"e in

these hidden nodes! idden nodes are also used to interene

beteen the eAternal input and the netor8 output ithrespe"t to the netor8Cs $anner! he stru"ture o $ultilayer

eedorard netor8s ith one hidden layer as in i#ure '!

Figure #. *'(ical of multila'er feedforward networks [)].

2.1.3. $e%urrent Network wit& No-Self Feed'a%k Loo(

and No )idden Neuron

7 re"urrent neural netor8 has at least one eedba"8 loop!

t $ay "onsist o a sin#le layer o neurons ith ea"h neuron

eedin# its output ba"8 to all the input neurons as illustrated

in i#ure 9! he eedba"8 loops in"reased the learnin# "a-

pability o the netor8 and on its peror$an"e! ?esides,

these eedba"8 loops are also asso"iated ith unit delayele$ents (4

-1) hi"h result in a nonlinear dyna$i"al beha-

ior in a "ondition hen neural netor8 "ontains nonlinear

units!




Figure *. *'(ical of multila'er feedforward networks [)].

2.1.4. $e%urrent Network +it& )idden Neuron

his stru"ture distin#uishes itsel ro$ part (iii) ith

hidden neurons! he eedba"8 "onne"tions ori#inate ro$

the hidden neurons and also ro$ the output neurons! hestru"ture illustrated in i#ure 10!

Figure 1,. *'(ical of multila'er feedforward networks [)].

n neural netor8s, the input layer is passie hile the

hidden nodes and output layer are a"tie and nor$ally been

a"tiated by transer un"tion! hese a"tie nodes ill

$odiy ei#ht and bias alues to an opti$u$ nu$ber here

the netor8 is best or8s at!here are $any a"tiation

un"tions "an be applied su"h as radial basis (radbas),

"o$petitie transer un"tion ("o$pet), positie linear

(poslin), saturatin# linear transer un"tion (satlin) and $any

$ore! he $ost "o$$only used is in"ludin# hard-li$it

transer un"tion, linear transer un"tion and lo#-si#$oid

transer un"tion! eural netor8s need to be trained ith

suitable learnin# al#orith$ trainin# un"tions "orrespondin#

to a netor8 type! 7$on# o the trainin# un"tions are

#radient des"ent ba"8propa#ation (train#d),

Heenber#-ar>uadt ba"8propa#ation (trainl$), #radient

des"ent ith adaptie learnin# rule ba"8propa#ation

(train#da), rando$ order in"re$ental trainin# ith learnin#

un"tions (trainr) and so on!

his eAperi$ental or8s used eed orard neural

netor8 ith si#$oid hidden nodes and output neurons! t

has been trained by s"aled "on%u#ate #radient

ba"8propa#ation (trains"#) learnin# al#orith$! 7boe aresele"ted sin"e they are $ost "o$$only used in arious

appli"ation, suitable or this pro%e"t purpose and eApe"ted to

be $u"h ei"ient!

2.3. "et&odology

Features eAtra"tion o *.< si#nals hae been done to

"olle"t ne"essary data re>uired or *.< analysis usin#neural netor8! n a truth a"t, hundreds o input eatures

"ould be eAtra"ted ro$ the *.< si#nal! all o the eatures

are ta8en into "onsideration, identii"ation inor$ation pro-

ided ould be irreleant and so$e do not #ie $u"h si#-

nii"ant to the netor8! Further$ore, the trainin# duration

also ill be $u"h lon#er! eural netor8s also adaptie to a

non-linear and irreleant data ith a de#ree o toleran"e! Iet,

their peror$an"e ill be hi#hly ei"ient hen #iin# only

appropriate and sele"ted inputs E'!

First o all, seeral types o *.< si#nals obtained ro$

healthy and unhealthy patients! ?asi"ally, *.< si#nal "on-

tains o P@ pea8s! n order to dete"t the pea8 "orres-

pondin#ly, an *.< si#nal has been diided into three se#-

$ents! he irst se#$ent is P aeor$, the se"ond se#$ent

is @ aeor$ and the last one is aeor$! + ae-

or$ is so$eho eAist in *.< si#nal, but it "an be i#nored

as it does not really $a8e any si#nii"ant in "ardia" dia#no-

sis! he respe"tie aeor$ ater separatin# ro$ the

ori#inal si#nal is shon belo!

Figure 11. + waeform.

Figure 12. -S waeform.

Figure 13. waeform.

he neAt sta#e is eAtra"tin# i$portant data eatures and

"hara"teristi"s o those aeor$s usin# $anual

"o$putation! he $anual "o$putation has been eriied and

approed by "ardiolo#ist, but it is $ore re"o$$ended to use

Hab=* sotare to peror$ auto$ated eAtra"tion in

order to #et a""urate alues and pre"ise "o$putation! hese

eatures sele"ted are a$plitudes, durations, #radients and

polarity! hen, all the alues ill be nor$ali4ed ithin the

ran#e ro$ 0 to 1 only! 7ll o these eatures then ed into the

neural netor8 syste$ as its inputs ith "ertain desired

tar#et deined! he *.< data si#nals are "olle"ted and ar-

ran#ed in the or$ o nu$bers! here are 20 types o *.<si#nals inoled in this eAperi$ental si$ulation ith &9




sa$ples (1G sa$ples o P ae, 20 sa$ples o @ ae

and 12 sa$ples o ae)!hen, the si$ulation to #et *.<

aeor$ as peror$ed by usin# i"rosot *A"el or

easier urther analysis! ?elo is shon typi"al eAa$ple o

*.< si#nals and ho eatures eAtra"tion is bein# a""o$-

plished! he sa$e $ethod o eatures eAtra"tion then been

applied to the rest o *.< si#nals! his "o$putation is >uite

ti$e "onsu$in# and thus better syste$ or eAtra"tion anal-

ysis is hi#hly re"o$$ended to be i$ple$ented in the uture!

eerrin# to this i#ure belo, real pea8 has been $ar8ed red

in "olour! he pro"ess to identiy real pea8 is ia te$plate

$at"hin# and "ardiolo#ist erii"ation! he eAa$ined si#nal

is "o$pared ith the sour"e or te$plate i$a#e! hose

aeor$s possessed hi#h si$ilarity in their $orpholo#i"al

attributes and eatures ill be "lassiied a""ordin#ly!

o$e o the aeor$s hi"heer not hae sour"e te$-

plate ill #et eriied by "ardiolo#ist ater $ar8in# the eA-

pe"ted real pea8 based on theoreti"al 8noled#e or pea8

"riteriaCs!

Figure 14. Sam(le of ECG sinal.

7$plitude

P J -0!223G&22

@ J -0!1001

J -1!1&'99'6

J -2!2906692

J -0!&30'G&6

Duration

P ae J dieren"e o A-aAis alues

0!0012 K 0!000G J 0!000

@ "o$pleA ("onsidered as one "o$plete aeor$)

@ (A-aAis) J 0!0022

(A-aAis) J 0!002'

@ duration: 0!002' K 0!0022 J 0!0006

ae J dieren"e o A-aAis alues

0!003G K 0!0031 J 0!0006

<radient

Figure 15. +re/rad +.

2 1

2 1

$ =−

−

'

0 0

0!2G6!136 !0&123106Pre #radP

0!000' 0!000G

0!13629

0!0001

− +− =

−

=

Figure 16. +ost/rad +.

0!223G&22 0!&&2110&$

0!001 0!0011

− +=

−

0!21'36'2Post #radP

0!0001

21'&

− =

−

= −

his "o$putation to ind pre-#radient and post-#radient o

other aes applied the sa$e $ethod as aboe!

PolarityDeinition: the aeor$ is positie, then "orrespond-



1& ar$i4i !7 et al.: 7 %ournal o real pea8 re"o#nition o ele"tro"ardio#ra$ (e"#) si#nals usin# neural netor8

in# pea8 is set as 1! the aeor$ is ne#atie, then the

pea8 is set as 0! Positie is reerred to sa$e pattern as the

nor$al aeor$, hile the shape oppose the "orrespond-

in# nor$al aeor$ is denoted as ne#atie! n this "ase,

polarity or ea"h aeor$ assi#ned as belo

P J 1

@ J 1

J 1

Data then been arran#ed usin# i"roso *A"el and ater

nor$ali4ation, the data then be ed as an inputs to neural

netor8 pattern re"o#nition syste$! his nor$ali4ation is

i$portant to ensure the alues o the data are in beteen the

"ertain ran#e! hus, it is easier to train the neural netor8s

ei"iently as ell as easier or the netor8 syste$ to learn!

he netor8 ill learn input "hara"teristi"s and attributes o

the "orrespondin# aeor$s! or eAa$ple the "hara"te-risti"s o P ae are #ien to the netor8 or trainin#, the

tar#et o P ae ill be put as 1 and the other is 0! Ln"e the

P ae is "orre"tly dete"ted, thus the real pea8 o P is easily

obtained by the hi#hest pea8 o that aeor$! his is sa$e

#oes to other aeor$s!

&# 'esult

Figure 1!. Network (erformance 2SE s num!er of e(oc"s3.

etor8Cs "oni#uration has been detail identiied ith '0hidden neurons, 2 layers eed orard, trained ith s"aled

"on%u#ate #radient ba"8 propa#ation (trains"#) ith si#$oid

type o hidden nodes and output neurons! &9 sa$ples hae

been used ith input eatures, des"ribed as a$plitude,

duration, pre-#radient, post-#radient and polarity! he per-

or$an"e o the netor8 is ealuated in $ean s>uared error

and "onusion $atri"es! etor8 ill be retrained to a"hiee

as per desired tar#et!

i$ulation result shoin# that trainin# and testin#

peror$an"e 8eep de"reasin#! n other ords, it proed that

the netor8 is learnin#! Durin# testin#, it tried to a"hieed

the tar#et approAi$ately as hat has been trained! 7 part

ro$ that, it is seen that best alidation peror$an"e, hi"h

is the s$allest dieren"e o desired tar#et ith the netor8

output is at 1!1103A10-1

ater #oin# throu#h 2G iterations! 7t

this point, the netor8 possessed the ability to #enerali4e

ery ell ater peror$an"e be"o$es $ini$i4ed to the #oal!

Figure 1#. Network trainin state.

esult aboe presented trainin# state o the netor8! he

plot depi"ted the trainin# state o the netor8 ro$ a trainin#

re"ord! he $ini$u$ #radient rea"hed at epo"hs 33 at a

alue o G!GG11G A 10-2

! =alidation "he"8s are at 6 also o"-

"urred durin# 33th iteration! he netor8 has been ell

trained and learnin# to "lassiy respe"tie inputs to the "or-respondin# tar#et! t stops hen $ini$u$ #radient has been

rea"hed to aoid netor8 ro$ oerittin# and be"o$es

un"ontrolled! n neural netor8 "o$putin#, alidation is

used to ensure the netor8 able to #enerali4e respe"tie

inputs $appin# to the "orrespondin# tar#et! =alidation ill

halt the trainin# hen #enerali4ation stops i$proin#!

Figure 1*. Confusion (lot matri0.




7 "onusion $atriA depi"ted "learly the a"tual ersus

predi"ted "lass alues! t is also presented the "lasses hi"h

are "orre"tly "lassiied and $is"lassiied! hus, it is enablin#

us to see ho ell the $odel predi"ts the out"o$es E9!

here are three aeor$s that need to be re"o#ni4ed by

neural netor8s, hi"h is aeor$ o P, @ and ! he

real pea8 is dete"ted by the hi#hest pea8 o the aeor$!

his is dierent or @! Ln"e the @ aeor$ is "or-

re"tly identiied by neural netor8 syste$, thus it is 8non

that the hi#hest pea8 should be the real pea8 o , the pea8

beore is @ and the pea8 ater the pea8 should be pea8!

3 dataset has been used or trainin# purposes, G or alida-

tion and G or testin# phase! ?ased on the result aboe, it

shoed that durin# trainin# session, there are 30 set o data

are "orre"tly "lassiied and only data are $is"lassiied! n

alidation, data are "orre"tly "lassiied out o G hile

durin# testin# phase, data too are "orre"tly "lassiied a"-

"ordin#ly hile ti"e o $is"lassii"ation happened! e-

eral a"tors "ontribute to netor8Cs i$proe$ent ill bein"luded in a dis"ussion part! he per"enta#e a""ura"y o

ea"h pro"ess (trainin#, alidation and testin#) is also "an be

seen in the "onusion plot $atriA aboe! Lerall netor8

peror$an"e shoin# that &0 data are "orre"tly "lassiied as

desired hile 9 data are $is"lassiied! t peror$ed #ood

"lassii"ation ith total a""ura"y o '1!6B!

Figure 2,. eceier 4(eratin C"aracteristic 4C3.

7boe is shon the out"o$e o re"eier operatin# "ha-

ra"teristi" (L.) or this pro%e"t! he L. "ure a"ts as

unda$ental tools or dia#nosti" ealuation or positie test

hi"h plotted true positie rate s alse positie rate E9, E10!

t rele"ts to a sensitiity and spe"ii"ity o the netor8! rue

positie rate reerred to real pea8 si#nals "orre"tly identiied

as its real pea8 hile alse positie rate reerred to non real

pea8 o the si#nals in"orre"tly identiied as real pea8!

(# )is!ussin

he result or this pro%e"t "annot a"hiee 100B or ery

hi#h per"enta#e o a""ura"y sin"e s$all dataset as used!

hus, so$e re"o$$endations "ould be su##ested to i$-

proe netor8Cs peror$an"e in order to obtain approA-

i$ately a""urate outputs! here are in"ludin# in"rease the

nu$bers o hidden neurons and retrain the netor8 seeral

ti$es! Further$ore, use lar#er data set so that the netor8

ill learn $ore and eApose to enou#h trainin#s! Do try a

dierent trainin# al#orith$ as ell as ad%ust the initial

ei#hs and biases to ne alues! hen, train the netor8s

a#ain or seeral ti$es until it rea"hes the desired tar#et! n

addition, it is re"o$$ended to peror$ auto$ated eatures

eAtra"tion usin# respe"tie sotare su"h as Hab=* or

et" or a""ura"y and >ui"8 eAtra"tion purposes!

*# +n!lusin

eural netor8 pattern re"o#nition is suitable sotare

ith hi#h ability to "lassiy "ertain input patterns into a

"orrespondin# output tar#et ith oerall a""ura"y o '1!6B!he trainin# a""ura"y is '!GB, alidation a""ura"y is G1!&B

hile the testin# a""ura"y is G1!&B too! t "an be "on"luded

that the real pea8 o *.< si#nals "an be identiied by

trainin# the netor8 a""ordin#ly!

A!n"ledgement

he appre"iation #oes to absolutely $y $ain superisor,

Dr! yed ahal a4li 7lhady or proides endless help in-

"ludin# $otiation and #uidan"e and also not or#et to

"o-superisor as ell as ield superisor or so$e supported

ideas dire"tly or indire"tly! y deepest #ratitude then eA-tends to inistry o "ien"e, e"hnolo#y and nnoation,

#oern$ent o alaysia or illin# to sponsor $e

throu#hout $y study, ithout the s"holarship, this pro%e"t

$i#ht a"e dii"ulty and "ould not be a""o$plished in a

"o$ortable $anner! han8 you ery $u"h to those in-

oled!

'e%eren!es

E1

7 ebsite onhttp://biolo#y!about!"o$/library/or#ans/heart/blatrionode!ht

$, i$a#e "ourtesy o .arolina ?iolo#i"al upply / 7""ess*A"ellen"e!

E2 .hun#, !, and i"h, !! ntrodu"tion to the "ardioas-"ular syste$! 7l"ohol ealth and esear"h orld1&(&):269-2G6, 1990!

E3 nor$ation,http://biolo#y!about!"o$/od/hu$ananato$ybiolo#y/ss/heartanato$y2!ht$

E& a4har ?!ayel, oha$ed *!*l-?ouridy, *.< i$a#es"lassii"ation usin# eatures eAtra"tion based on aelettransor$ation and neural netor8, 7H 06 nternational.oneren"e, 13-1 une 2006, har$ *l hei8h, *#ypt,10-10G!

E a%esh <hon#ade, =isha8ar$a, Dr! ?abasaheb, 7 brie




Peror$an"e *aluation o *.< Feature *Atra"tion e"h-ni>ues or 7rtii"ial eural etor8 ?ased .lassii"ation!

E6 .hristos ter#iou and Di$itrios i#anos, 7 eport on eural etor8s!

EG

i$on ay8in, 7 boo8 o eural etor8s 7 .o$prehen-

sie Foundation, e"ond *dition, 1999!

E'

nternational %ournals on *7 ransa"tions on yste$s,ssue 1, =olu$e &, anuary 200, 1109-2GGG, papertitled P, @, , , and pea8s re"o#nition o *.< usin#

?F ith sele"ted eatures, 13G!

E9 !P! .hala, Depart$ent o *le"tri"al *n#ineerin#, ndiannstitute o e"hnolo#y, oor8ee, 2&G66G ndia, Para$ete-ri4ation and -pea8 error esti$ations o *.< i#nals usin#ndependent .o$ponent 7nalysis, .o$putational and a-the$ati"al ethods in edi"ine, =ol! ', o! &, De"e$ber

200G, 263-2'!

E10 *Aer"ise on 7rtii"ial eural etor8s, nor$ation onrad!ihu!edu!#r/ilead$in/labsiles/de"isionMsupportMsyste$s/lessons/neuralMnets/s!pd!

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