Dr.S.Sumathi 1, Mrs.V.Agalya 2 Mahendra Engineering College, Mahendhirapuri, Mallasamudram

Transcription

1 Detecton Of Myocardal Ischema In ECG Sgnals Usng Support Vector Machne Dr.S.Sumath 1, Mrs.V.Agalya Mahendra Engneerng College, Mahendhrapur, Mallasamudram Abstract--Ths paper presents an ntellectual dagnoss system usng rule based evaluvaton of myocardal schema n electrocardogram (ECG) sgnals usng wavelet transform. Ths method s manly through the computaton of an ndcator related to the area covered by the ST-wave curve. The algorthm s healthy to acquston nose, to wave form morphologcal varatons and to baselne wanderng. To fnd the man computaton has been mplemented to smple fnte mpulse response flter. The ncluson of rule based system n the complex nvestgatng algorthms yelds very nterestng recognton and classfcaton of bomedcal engneerng. The results gve mportance to that the proposed model llustrates potental advantage to dentfy the myocardal Ischema. The senstvty 93.01% and postve predctvty of s acheved. Keywords: ECG, Wavelet Transform, Support Vector Machne, Myocardal Ischema. I. INTRODUCTION Myocardal Ischema (MI) s the most common cardac dsorder and ts early dagnoss s of great mportance. It s defned by a reduced blood flow of the myocardum whch causes alteratons n the ECG sgnal, such as devatons n the ST segment and changes n the T wave. MI s consdered to be a major complcaton of the cardac functon and a prme cause for the occurrence of cardac nfarcton and dangerous cardac arrhythmas has been ponted out by Cohen (1988). The man characterstc of schema at the cellular level s the depolarzaton of the cellular restng membrane potental. Ths causes a potental dfference between the normal and schemc tssue whch, n turn, causes the flow of an njury current (Coumel and Garfen 1990). Ths njury current s manfested n the electrocardogram (ECG) by an ST depresson or elevaton, dependng on the anatomcal poston of the heart and the dpole s poston wth respect to the recordng electrodes (Paul 1998, Gu-Young Jeong and Kee-Ho Yu 007, Tapobrata et al 009). Thus, there are cases n the 1-lead standard electrode system where the ST depresson s not evdent n schemc beats, whle ST depresson may exst when schema s not present such as can happen wth leads III and a VF due to patent poston has been ponted by Zmmerman and Povnell (004). Ischemc epsodes could be acute ones that should be detected mmedately when the patent s n a Crtcal Care Unt (CCU) envronment, but also n a Holter database schemc epsodes should be relably and correctly detected. Although schema detecton from ST analyss alone s dffcult to accomplsh and has to be accompaned by a number of bochemcal and other examnatons, ECG stll remans one of the basc bosgnals for adng the clncal staff n a CCU envronment. Major problems contrbutng to poor detecton of the ST segment n the ECG can be dentfed as follows: Slow baselne drft Nose Sloped ST changes Patent dependent abnormal ST depresson levels and Varyng ST-T patterns n the ECG of the same patent. The ST level change epsodes lastng several seconds or sometmes some mnutes, s an mportant ndcaton n the dagnoss of myocardal schema. A typcal ST segment s comprsed of the followng fducal ponts: S, J, ST area, T pont, T peak, and ST segment level measurement. Fgure 1 shows the relevant ST segment ponts n ECG sgnal. In order to dentfy these fducal ponts, we use the WT method. WT has been successfully appled to the dgtal processng of ECG sgnals for ts ablty to localze the sgnals n both tme and frequences. The ST level change epsodes lastng several seconds or sometmes some mnutes, s an mportant ndcaton n the dagnoss of myocardal schema. IJRASET 013: All Rghts are Reserved 387

2 Fgure 1 The relevant ST segment ponts n ECG sgnal A. Wavelet Transform The ECG sgnals are consdered as representatve sgnals of cardac physology, whch are useful n dagnosng cardac dsorders. The complete way to dsplay ths nformaton s to perform spectral analyss. The Wavelet Transform (WT) gves extremely general technques, whch can be mplemented to many tasks n sgnal processng. The ECG sgnal, consstng of lot of characterstc ponts, can be compressed nto a few ponts. These ponts characterze the behavour of the ECG sgnal. Ths feature of usng a lesser number of parameters to represent the ECG sgnal s partcularly mportant for recognton and dagnostc functons. The WT can be thought of as an extenson of the classc Fourer transform, but nstead of workng on a sngle scale (tme or frequency), t works on a multple-scale bass. Ths multple-scale feature of the WT allows the decomposton of a sgnal nto a number of scales, every scale representng a partcular coarseness of the sgnal under ths study. The procedure of multresoluton decomposton of a sgnal x[n] s schematcally shown n Fgure. Every stage of ths scheme conssts of two dgtal flters and two down samplers by. The ntal flter, the dscrete mother wavelet s g[n], hgh pass n nature, and the second, h[n] s ts reflect verson, low-pass n nature. The down sampled outputs of frst hgh pass and low-pass flters gve the detal, d1 and the approxmaton, a1, respectvely. The frst approxmaton, a1 s more decomposed and ths process s contnued as shown n Fgure.. x[n] h(n) g(n) a 1 [n] d 1 [n] h(n) g(n) Fgure Three level wavelet decomposton tree B. ECG Database The tranng set was constructed usng patterns from the European Socety of Cardology (ESC) ST-T Database. The ECGs ncluded n ths database are -hour long recordngs dgtzed at 50 Hz. Ths database s ntended to be used for the evaluaton of algorthms for schema analyss based on ST and T wave changes. The database conssts of 90 contnuous two channel records. The leads used ncluded modfed leads V1, V, V3, V4, and V5 and modfed lmb leads MLI and MLIII. Each record contans at least one ST or T schemc epsode. Specfcally, for the ST epsode annotatons, the expert cardologsts used the followng crtera: a [n] d [n] h(n) g(n) a 3[n] d 3[n] ST segment devatons are measured n relaton to a reference normal waveform selected from the frst 30 s of each record. Measurements of ST devaton are taken 80 ms after the J-pont or n the case of tachycarda 60 ms after the J-pont. ST epsodes must contan an nterval of at least 30 s durng whch the absolute value of the ST devaton s no less than 0.1 mv. The begnnng and the end of each epsode are annotated searchng backward and forward, respectvely, from the ST epsode, untl a beat s found wth absolute ST segment devaton less than 0.05 mv. Workng ndependently, the two specalst-annotators vsually checked the full dsclosure prntouts and manually nserted annotatons ndcatng changes n ST and T morphology, rhythm, and IJRASET 013: All Rghts are Reserved 388

3 sgnal qualty. Annotatons from the two cardologsts were compared and the dfferences were resolved by a thrd cardologst. C. Theory Of SVM The SVM technque, was orgnally proposed essentally for classfcaton problems of two classes but was found to be useful to deal wth non-lnearly separable cases too. Gven a set ofponts whch belong to ether of two classes, a lnear SVM fnds the hyperplane leavng the largest possble fracton of ponts of the same class on the same sde, whle maxmzng the dstance of ether class from the hyperplane[5-10]. N u( x) yk( x, x) b N j 1 1 F j y jk( x j, x ) y Case1 0 ( F b) y 0 Case 0 C ( F b) y 0 Case3 C ( F b) y 0 And by further classfcaton accordng to the possble combnaton of alpha and b F for I 0 I1 I b F for I 0 I3 I 4 y. We can have: II. MATERIAL AND METHODS The method carred out n ths thess s gven. The algorthm s developed n fve steps: ECG Sgnal Preprocessng ECG Feature Extracton Detecton of Beat Wndow Classfcaton and Ischema Epsode Identfcaton. A. ECG Sgnal Preprocessng In the frst stage, preprocessng of the ECG recordng s performed to acheve nose removal, artfact rejecton and extracton of the sgnal features whch are used for beat characterzaton. For nose cancellaton procedure n ths work cubc splne wavelet flterng for elmnatng the low and hgh frequency components for nose cancellaton s used n ths work. B. ECG Feature Extracton The second stage detecton of schemc epsodes wth hgh senstvty and specfcty, the correct R peak, S pont, and J pont detecton s needed. Dfferent algorthms are used for R-peak detecton such as Pan-Tompkns and Dervatve-based methods. When these methods are used, the ST segment s assumed to begn at 60 ms after the R-peak n normal snus rhythm case. In the case of tachycarda (RR nterval < 600 ms), the begnnng of the ST segment s taken at 40 ms after the R peak. These values are n general agreement wth the recommendaton of the European ST-T database and wth the observatons n (L She et al 00, Mahammad Karm Mordant et al 009, Gonzalez et al 003). But ths method s not accurate enough and the heart rate must be contnuously computed. For solvng these problems a wavelet method for R peak, S pont, and J pont s presented here. In ths method, ECG sgnal s decomposed to S = 1 3 scales by WT algorthm usng dyadc splne wavelets. In S = scales, R wave has the hghest IJRASET 013: All Rghts are Reserved 389

4 ampltude whle the hgh frequency noses are decreased greatly and the low frequency noses are weak, so the R wave s extracted at S= scale. Low-frequency and slow-changng T wave s usually nfluenced by low ampltude and hgh frequency dsturbances, whch can be avoded at larger scales such as S = 3. S wave s hgh-frequency low-ampltude, and ts energy s mostly at S = 5 scale. So the S wave s extracted at S = 4 scale. R peak extracton s based on the relaton between the sgnal sngularty and ts WT, QRS complex wave corresponds to maxma and mnma termnatons at S = scale, and R peak corresponds to the zero-crossng pont of the maxma and mnma termnatons wth a fxed delay. ST segment s composed of S peak, J pont, the begnnng of the T wave, T peak and ST voltage. The extractve scheme of the feature ponts s llustrated n detal as follows. S peak s located at the frst downward peak after the zero-crossng pont of R peak at S = scale.tpeak s the frst zero-crossng pont of maxma and mnma termnatons after R peak at S = 3 scale. C. Detecton Of Beat The thrd stage has the classfcaton of each beat as normal or schemc based on a set of rules used by cardologst for locatng ST epsodes to dagnose schema. The two rules are framed based on ST devaton beng measured relatve to ST devaton n the reference template whch has been constructed from the frst 30s of each record. The extractve results of sx dfferent shapes of ST segment: normal, downslopng depresson, horzontal depresson, upslopng depresson, concave elevaton, convex elevaton were shown n Fgures.1 to.4. The frst rule refers to negatve ST devaton, n whch ST devaton s more than 0.08mV below the soelectrc lne and has an angle larger than 65 degree measured from vertcal lne, t s consdered as negatve ST devaton or ST depresson. The second rule classfes beat as schemc f ST devaton s more than 0.08 mv above the soelectrc lne has been ponted out by Goldberger (006). Voltage Tme (sec) (a) Tme (sec) (b) Fgure.1 (a) Normal ECG sgnal (b) Downslopng Depresson Voltage Fgure. ST Horzontal Depresson and Upslopng Depresson Tme (sec) Tme (sec) IJRASET 013: All Rghts are Reserved 390

5 Voltage Fgure.3 ST Concave and convex elevaton where - Poston of S peak Tme (sec) Tme (sec) - Poston of R peak * - Poston of T peak - Poston of J pont D. Wndow Classfcaton Accordng to European Socety of Cardology (ESC) recommendaton, a ST epsode must nclude an nterval of at least 30 seconds contanng schemc beats. Therefore, we use an adaptve movng wndow of 30 seconds that starts sldng beat-by-beat from the frst cardac beat to examne whether there exsts a sequence of schemc beats lastng more than 30 seconds. The wndow s classfed as schemc f the number of schemc beats s more than 75% of all beats n the wndow. E. Ischema Epsode Identfcaton In the fnal stage, we fnd all sequences of schemc wndows. If the duraton of normal ntervals appeared between two schemc wndows s less than 30 seconds, then a mergng technque wll be actvated to avod fragmentaton of the ST epsodes. After epsode dentfcaton, we annotate the begnnng and the end of each epsode. The begnnng s located by searchng backward from the pont at whch the absolute ST devaton frst exceeds 0.08 mv. The search contnues untl a beat s found for whch the absolute ST devaton s less than 0.05 mv. The end of each epsode s located by searchng forward from the pont at whch the absolute ST devaton last exceeds 0.08 mv. III. RESULTS AND DISCUSSIONS In the numercal experments, we have used the ECG data from the MIT-BIH Arrhythma Database correspondng to the normal heartbeat and myocardal arrhythmas. Each type heartbeat was extracted from the record whch contaned most beats of ths type.then each type heartbeat has a data set that contans many heartbeats. Due to the scarcty of data correspondng to some beat types the number of data belongng to each heartbeat type was varable. We chosen 70% of data set to be tranng data and 30% of data set to be testng data. The algorthm was tested usng a subset of ESC ST-T database. Ths database contans ECG recordngs wth annotated schemc epsodes. To evaluate the performance of the proposed classfer, two measures were used and defned as follows: Senstvty % TP TP FN (3.1) TP Postve Predctvty(%) = TP FP (3.) where TP, FN and FP are defned n Table 3.1. In order to derve ST epsode senstvty and postve predctvty, an epsode-byepsode comparson was needed. Accordng to Amercan Natonal Standard, overlap exsts durng any nterval n whch both the IJRASET 013: All Rghts are Reserved 391

6 reference and algorthm annotatons ndcate that an ST change s n progress. Events match for the purposes of measurng senstvty when the perod of overlap ncludes ether the reference marked extremum or at least 50% of the length of the referencemarked event. Events match for the purposes of measurng postve predctvty when the perod of overlap ncludes ether the algorthm-marked extremum or at least 50% of the length of the algorthm-marked event (ANSI/AAMI EC57, 1998). The processng tme for ECG recordngs was expressed as mean value one standard devaton. Table 3.1 ST epsode Senstvty and Postve Predctvty matrx Algorthm Reference ST Epsode Not Epsode ST Epsode TP FN Not Epsode FP TN The proposed method s evaluated as follows: we used 40 ECG records of ESC ST-T database. Each record conssts of two-channel two-hour ECG recordngs wth schemc epsode annotaton. Table 3. shows the results of applyng our method to each record. For each ECG recordng, the senstvty and postve predctvty values are gven n terms of percentages Record Table 3. Healthy Beat Number Performance of the epsode detecton method for 40 recordngs of the ESC ST-T Database Ischema Beat Number (TP) IJRASET 013: All Rghts are Reserved 39 FN FP Senstvty (%) Postve Predctvty (%) e e e e e e e e e e e e e e e e e e e e e

7 e e e e e e e e e e e e e e e e e e e Total When aggregate gross statstcs was used, we obtaned 90.01% and % for epsode senstvty and postve predctvty. The tme needed for the processng of each ECG recordng was 455s 91s. IV. CONCLUSION Ths paper presented an effcent system for the detecton of Myocardal Ischema epsodes n ECG sgnal usng SVM. Snce the detecton algorthm s based on the rules used by cardologsts for dagnosng schema n ECG records, t s useful as a dagnostc tool to ad the physcan n the analyss of ST changes. Short processng tme and acceptable accuracy of the proposed method, are ts man advantages and enables t to be used n real tme schemc epsodes detecton systems and relable clncal montorng of the patent status REFERENCES [1] [1] Bomedcal Engneerng - Applcatons, Bass and Communcatons. [] [] Coumel P. and Garfen O. (1990), Electrocardography: Past and Future, Annuals of the New York, Academy of Scences, Vol. 601, New York. [3] [3] Paul J.S. (1998), Egen Flter based Extracton of ST Segment Waveform from Surface ECG s, Proceedngs of the 0 th Annual Internatonal Conference of the IEEE Engneerng and Bology Socety, Hong Kong, October 9-30, November 1, Vol. 1, pp [4] [4] Gu-Young Jeong and Kee-Ho Yu (007), Morphologcal Classfcaton of ST segment usng Reference STs Set, Proceedngs of the 9 th Annual Internatonal Conference of the IEEE Engneerng n Medcne and Bology Socety, Lyon, August 3-6, pp [5] [5] Tapobrata, Upendra Kumar and Hrshkesh Mshra (009), Analyss of ECG Sgnal by Chaos Prncple to help Automatc Dagnoss of Myocardal Infarcton, Journal of scentfc and Industral Research, Vol. 68, pp [6] [6] Zmmerman M.W. and Povnell R.J. (004), On Improvng the Classfcaton of Myocardal Ischema Usng Holter ECG Data, Computers n Cardology, Vol. 31, pp [7] [7] L Sh, Cenyu Yang, Jnyng Zhang and Hu L (007), Recognton of ST Segment of Electrocardogram Based on Wavelet Transform, Lfe Scence Journal, pp IJRASET 013: All Rghts are Reserved 393

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